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ECCV 2026

Enhancing prompt-image alignment evaluations via cyclic mutual information maximization

Xingran Liao, Duanyu Feng, Mingliang Zhou, Sam Kwong, Weisi Lin

Assessing AI-generated images remains a signi_x001C_cant chal-lenge because both visual quality and prompt-image alignment are vital.Existing metrics prioritize visual quality, but the exploration of semanticalignment with user prompts remains limited. To address this issue, wepropose a novel cyclic mutual information maximization framework forAI-generated image quality assessment (CMIM-AIGIQA). Unlike exist-ing methods, our framework focuses on promoting e_x001B_ective multimodalinformation fusion in the deep feature domain, which has cyclic mutualinformation maximization phases. In the forward phase, we maximize themutual information between text and image features to generate re_x001C_ned,cross-aware representations. The re_x001C_ned features are then integratedthrough a fusion network. In the backward phase, the mutual informationbetween the joint embedding and every single modality is maximized toensure that the joint embedding retains critical semantic and visual cues.Experiments on three datasets demonstrate that our method e_x001B_ectivelybridges the gap between visual quality and prompt-image alignment. Thecode is available at https://github.com/Buka-Xing/CMIM-AIGIQA.

ECCV 2026

Expert Weaving: Marrying Masked AutoRegressive and Diffusion Models for Unified Image Restoration

Xin Lu, Jie Huang, Jie Xiao, Dong Li, Xueyang Fu

All-in-one image restoration aims to handle diverse degra-dations within a unified framework. While Mixture-of-Experts (MoE)architectures offer a scalable path, existing designs predominantly relyon vanilla end-to-end fidelity-oriented experts that lack task-adaptivegenerative priors, limiting their ability to recover realistic structuresand textures in complex scenarios. In this paper, we first investigatecomplementary task-adaptive generative priors: Masked Auto-Regressive(MAR) experts are tailored for local degradations (e.g., shadow) by re-constructing semantic structures via clean local backgrounds; conversely,Diffusion experts excel at global degradations (e.g., noise) by recoveringtextural details through iterative denoising. Motivated by these insights,we propose Mixture-of-Synergy-Experts (MoSE), a novel framework thatdeeply integrates these complementary generative priors into the MoEarchitecture and synergizes MAR and Diffusion experts with vanillabackbones at both image and feature levels. MoSE is orchestrated by aDINOv3-guided semantic-aware router that adaptively regulates not onlyexpert coordination but also generation control: mask sizes are adjustedto handle varying scales of local semantic corruption, while iterationsteps are modulated according to degradation intensity. By synergistically"weaving" complementary experts, our approach achieves a controllablefidelity-perception trade-off. Comprehensive experiments demonstrate theeffectiveness of MoSE, achieving competitive performance across variousall-in-one image restoration benchmarks.

ECCV 2026

Direct Autoregressive Diffusion Distillation via Error-aware Causal Pretraining

Jiaxing Li, Kaichen Huang, Baixin Xu, Zexiang Liu, Xianglong He, Zile Wang, Junyao Gao, Yang Liu, Ying He, Bo An, Yangguang Li

Real-time autoregressive (AR) video diffusion has progressedrapidly. Existing approaches typically distill pretrained bidirectional videofoundation models into few-step causal students; however, naive distilla-tion often collapses due to architectural mismatch. To obtain stable ini-tialization, prior methods rely on ordinary differential equation (ODE)trajectory matching, which incurs substantial computation and can in-troduce errors by forcing the student to imitate global trajectories. Inthis paper, we bypass trajectory matching stage by pretraining a robustcausal AR model that equips the student for direct few-step distillation.Moreover, to bridge the gap between pretraining and distillation, we pro-pose Error Forcing, an error-aware and parallelizable AR video diffu-sion framework. During training, Error Forcing injects controlled residualerrors into the conditioning context, approximating the inference-timehistory distribution. This enables the causal model to learn from de-graded yet clean contexts, improving robustness to accumulated errorswithout sacrificing parallel training throughput. With this initialization,we can perform direct distillation, and the student and teacher/criticnaturally operate under consistent conditioning distributions. Extensiveexperiments show that our method outperforms existing baselines in gen-eration quality during both the pre-training and distillation stages.

ECCV 2026

Rapidly Deploying On-Device Eye Tracking by Distilling Visual Foundation Models

Cheng Jiang, Jogendra Nath Kundu, David Colmenares, Fengting Yang, Joseph Robinson, Yatong An, Ali Behrooz

Eye tracking (ET) plays a critical role in augmented andvirtual reality applications. However, rapidly deploying high-accuracy,on-device gaze estimation for new products remains challenging becausehardware configurations (e.g., camera placement, camera pose, and il-lumination) often change across device generations. Visual foundationmodels (VFMs) excel on natural-image benchmarks and offer a promis-ing path to rapid training and deployment; yet, we find that off-the-shelfVFMs still struggle to reach high accuracy on specialized near-eye in-frared images. To close this gap, we introduce DistillGaze, a frameworkthat distills a VFM using labeled synthetic data and unlabeled real datafor rapid, high-accuracy on-device gaze estimation. DistillGaze proceedsin two stages. First, we adapt a VFM into a domain-specialized teacherusing synthetic gaze labels and unlabeled real images. Synthetic dataprovide scalable, high-quality gaze supervision, while unlabeled real databridges the synthetic-to-real domain gap. Second, we train an on-devicestudent from both teacher guidance and self-training. Evaluated on alarge-scale crowd-sourced dataset spanning more than 2,000 participants,DistillGaze reduces median gaze error by 58.6% relative to synthetic-only baselines while maintaining a lightweight 256K-parameter modelsuitable for real-time on-device deployment. More broadly, DistillGazeoffers an efficient path to training and deploying ET models that adaptto hardware changes, and a recipe for combining synthetic supervisionwith unlabeled real data in on-device regression tasks.

ECCV 2026

Making Partial-Label Datasets Easier: A Simple Yet Highly Effective Data Augmentation for Deep Partial-Label Learning

Dong-Dong Wu, Zhaoyi Li, Xiang Li, Zhiqiang Shen

Partial-label learning (PLL), which refers to the classifica-tion task where each training instance is associated with a set of candidate-labels, among which only one is correct. While recent advances in deepPLL primarily focus on designing sophisticated disambiguation strate-gies, the role of PLL-specific data augmentation has been less explored.Most methods often treat data augmentation as a generic plug-in, over-looking its potential interaction with the label disambiguation processunique to PLL. To address this gap, we propose a local-ambiguity-awareaugmentation framework that consists of label identification and modelregularization modules and accommodates a wide range of PLL algo-rithms. Within this framework, a PLL-specific augmentation strategybased on a mixing-renormalization process is applied to partial-labeldata. Theoretically, we show that our augmentation can make the PLLdataset easier by reducing the local ambiguity degree, and it induces mul-tiple implicit regularizations during training thereby facilitating highlyeffective disambiguation. Extensive experiments on both benchmark andreal-world datasets demonstrate that our approach consistently improvesthe generalization performance of state-of-the-art PLL algorithms.

ECCV 2026

SupGRPO: Enhancing GRPO with Matching-based Online SFT for Text Spotting

Xudong Xie, Yuzhe Li, Jing Shi, Zhifei Zhang, Curtis Wigington, Zhaowen Wang

Text spotting requires both accurate text recognition and precise spatial localization. Current specialised spotters excel at predicting tight bounding boxes in natural scenes, but falter on complex or artistic text, whereas multimodal large language models (MLLMs) possess strong recognition capabilities yet remain weak at localisation. To equip the text spotter with general and powerful recognition capabilities and to maximize its localization ability, we explore two MLLM-based fine-tuning methods: Supervised Fine-Tuning (SFT) and reinforcement learning finetuning based on Group Relative Policy Optimisation (GRPO). An interesting finding is that SFT is less effective than GRPO at enhancing recognition, while GRPO is less effective than SFT at enhancing detection. To compensate for each other’s shortcomings, we introduce a joint training strategy, SupGRPO, which simultaneously optimizes the model using both SFT and GRPO. SupGRPO employs the specially designed reward functions and develops a matching-based online SFT applied solely to coordinate tokens. It both mitigates the reward sparsity problem of GRPO and avoids the instance order dependency problem of SFT. To evaluate particularly challenging cases, we curate ATS, a dataset for artistic text spotting. Experiments demonstrate that SupGRPO improves both text recognition and detection, and attains superior performance. Our code and dataset will be released at https://github.com/Psycho-9/SupGRPO.

ECCV 2026

Finite Difference Flow Optimization for RL Post-Training of Text-to-Image Models

David McAllister, Miika Aittala, Tero Karras, Janne Hellsten, Angjoo Kanazawa, Timo Aila, Samuli Laine

Reinforcement learning (RL) has become a standard tech-nique for post-training diffusion-based image synthesis models, as itenables learning from reward signals to explicitly improve desirable as-pects such as image quality and prompt alignment. In this paper, wepropose an online RL variant that reduces the variance in the modelupdates by sampling paired trajectories and pulling the flow velocityin the direction of the more favorable image. Unlike existing methodsthat treat each sampling step as a separate policy action, we considerthe entire sampling process as a single action. We experiment with bothhigh-quality vision language models and off-the-shelf quality metrics forrewards, and evaluate the outputs using a broad set of metrics. Ourmethod converges faster and yields higher output quality and promptalignment than previous approaches.

ECCV 2026

Cheers: Decoupling Patch Details from Semantic Representations Enables Unified Multimodal Comprehension and Generation

Yichen Zhang, Da Peng, Zonghao Guo, Zijian Zhang, Xuesong Yang, Tong Sun, Shichu Sun, Yidan Zhang, Yanghao Li, Haiyan Zhao, Wang Xu, Qi SHI, Yangang Sun, Chi Chen, Shuo Wang, Yukun Yan, XU HAN, Qiang Ma, Wei Ke, Liang Wang, Zhiyuan Liu, Maosong Sun

A recent cutting-edge topic in multimodal modeling is tounify visual comprehension and generation within a single model. How-ever, the two tasks demand mismatched decoding regimes and visualrepresentations, making it non-trivial to jointly optimize within a sharedfeature space. In this work, we present Cheers, a unified multimodalmodel that decouples patch-level details from semantic representations,thereby stabilizing semantics for multimodal understanding and improv-ing fidelity for image generation via gated detail residuals. Cheers in-cludes three key components: (i) a unified vision tokenizer that encodesand compresses image latent states into semantic tokens for efficient LLMconditioning, (ii) an LLM-based Transformer that unifies autoregressivedecoding for text generation and diffusion decoding for image generation,and (iii) a cascaded flow matching head that decodes visual semanticsfirst and then injects semantically gated detail residuals from the vi-sion tokenizer to refine high-frequency content. Experiments on popularbenchmarks demonstrate that Cheers matches or surpasses advancedUMMs in both visual understanding and generation. Notably, Cheersoutperforms the Tar-1.5B on the popular benchmarks GenEval and MM-Bench, while requiring only 20% of the training cost, indicating effectiveand efficient (i.e., 4× token compression) unified multimodal modeling.Code and checkpoints are available at github.com/AI9Stars/CHEERS.

ECCV 2026

Learning to Stylize by Learning to Destylize: A Scalable Paradigm for Supervised Style Transfer

Ye Wang, Zili Yi, Yibo Zhang, Peng Zheng, Xuping Xie, Jiang Lin, Yijun Li, Yilin Wang, Rui Ma

This paper introduces a scalable paradigm for supervisedstyle transfer by inverting the problem: instead of learning to stylizedirectly, we learn to destylize, reducing stylistic elements from artisticimages to recover their natural counterparts and thereby producing au-thentic, pixel-aligned training pairs at scale. To realize this paradigm, wepropose DeStylePipe, a progressive, multi-stage destylization frameworkthat begins with global general destylization, advances to category-wiseinstruction adaptation, and ultimately deploys specialized model adap-tation for complex styles that prompt engineering alone cannot han-dle. Tightly integrated into this pipeline, DestyleCoT-Filter employsChain-of-Thought reasoning to assess content preservation and style re-moval at each stage, routing challenging samples forward while discard-ing persistently low-quality pairs. Built on this framework, we constructDeStyle-350K, a large-scale dataset aligning diverse artistic styles withtheir underlying content. We further introduce BCS-Bench, a benchmarkfeaturing balanced content generality and style diversity for systematicevaluation. Extensive experiments demonstrate that models trained onDeStyle-350K achieve superior stylization quality, validating destyliza-tion as a reliable and scalable supervision paradigm for style transfer. Ourproject page: https://wangyephd.github.io/projects/DeStyle/index.html

ECCV 2026

Next-Frame Decoding for Ultra-Low-Bitrate Image Compression with Video Diffusion Priors

Yunuo Chen, Chuqin Zhou, Jiangchuan Li, Xiaoyue Ling, Bing He, Jincheng Dai, Li Song, Guo Lu

We present a novel paradigm for ultra-low-bitrate imagecompression (ULB-IC) that exploits the “temporal” evolution in gener-ative image compression. Specifically, we define an explicit intermediatestate during decoding: a compact anchor frame, which preserves the scenegeometry and semantic layout while discarding high-frequency details.We then reinterpret generative decoding as a virtual temporal transitionfrom this anchor to the final reconstructed image. To model this progres-sion, we leverage a pretrained video diffusion model (VDM) as a temporalprior: the anchor frame serves as the initial frame and the original imageas the target frame, transforming the decoding process into a next-frameprediction task. In contrast to image diffusion-based ULB-IC models, ourdecoding proceeds from a visible, semantically faithful anchor, which im-proves both fidelity and realism for perceptual image compression. Ex-tensive experiments demonstrate that our method achieves superior rate-distortion performance. On the CLIC2020 test set, our method achievesover 50% bitrate savings across LPIPS, DISTS, FID, and KID com-pared to DiffC, while also delivering a significant decoding speedup of upto ×5. Code will be released at https://github.com/UnoC-727/NeFIC.

ECCV 2026

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi

Multimodal CAD generation faces a fundamental scalability challenge. Design-history methods are confined to small annotated datasets, while BRep topology is discrete and non-differentiable. Meanwhile, millions of unannotated 3D meshes remain untapped, since existing CAD methods cannot leverage them without explicit CAD annotations. We propose DreamCAD, a multimodal generative framework that bridges this gap by representing shapes as C0-continuous Bézier patches with differentiable tessellation, enabling direct point-level supervision on large-scale 3D meshes without CAD-specific annotations. We further introduce CADCap-1M, the largest CAD captioning dataset with 1M+ GPT-5-generated descriptions to advance text-to-CAD research. DreamCAD achieves state-of-the-art performance on ABC and Objaverse across text, image, and point modalities, surpassing 75% user preference. Finally, we show that DreamCAD’s accurate, compact geometry enables topology recovery into production-ready CAD models, exportable as editable STEP files. Project page is available at https://s adilkhan.github.io/dreamcad2026/.

ECCV 2026

EvoTok: A Unified Image Tokenizer via Residual Latent Evolution for Visual Understanding and Generation

Yan Li, Ning Liao, Xiangyu Zhao, Shaofeng Zhang, Xiaoxing Wang, Yifan Yang, Junchi Yan, Xue Yang

The development of unified multimodal large language mod-els (MLLMs) is fundamentally challenged by the granularity gap be-tween visual understanding and generation: understanding requires high-level semantic abstractions, while image generation demands fine-grainedpixel-level representations. Existing approaches usually enforce the twosupervision on the same set of representation or decouple these two su-pervision on separate feature spaces, leading to interference and incon-sistency, respectively. In this work, we propose EvoTok, a unified imagetokenizer that reconciles these requirements through a residual evolutionprocess within a shared latent space. Instead of maintaining separatetoken spaces for pixels and semantics, EvoTok encodes an image intoa cascaded sequence of residual tokens via residual vector quantization.This residual sequence forms an evolution trajectory where earlier stagescapture low-level details and deeper stages progressively transition to-ward high-level semantic representations. Despite being trained on a rel-atively modest dataset of 13M images, far smaller than the billion-scaledatasets used by many previous unified tokenizers, EvoTok achieves astrong reconstruction quality of 0.43 / 0.25 rFID at 256×256 / 384×384resolutions on ImageNet-1K, respectively. When integrated with a largelanguage model, EvoTok shows promising performance across 8 out of 9visual understanding benchmarks, and remarkable results on image gen-eration benchmarks such as GenEval, GenAI-Bench, and DPG. Theseresults demonstrate that modeling visual representations as an evolvingtrajectory provides an effective and principled solution for unifying visualunderstanding and generation.

ECCV 2026

Bridging Online and Offline Handwriting via Differentiable Physical Rendering

Seonmi Park, Seunghyun Shin, Vihaan Misra, Dongmin Shin, Ukcheol Shin, Jean Oh, Hae-Gon Jeon

Realistic handwritten text generation plays an important rolein numerous applications, such as font design, biometric authentication,and robotic calligraphy. Existing methods are typically divided into twoindependent paradigms: online approaches that estimate handwritingtrajectories and offline approaches that synthesize realistic handwritingimages. While online models capture structural and temporal dynamics,they often lack fine-grained textures, whereas offline models reproducerealistic appearance but discard stroke order. However, unifying onlineand offline models remains challenging due to (1) the lack of an explicitphysical model linking stroke kinematics to pixel-level appearance and(2) the absence of paired trajectory–image datasets. Moreover, enablingend-to-end learning requires a differentiable rendering process acrossmotion and appearance domains. To address these challenges, we proposea compact physical brush model that bridges stroke dynamics and visualappearance, together with a differentiable rendering module that convertsstroke trajectories into stylized images. By integrating these components,we propose a unified online–offline handwriting generation frameworkvia differentiable brush rendering. The proposed framework consistsof four core modules: 1) a text-to-stroke generator that predicts thetarget stroke conditioned on the given text and style image, 2) a brushparameter observer that extracts brush model parameters from stylereferences, 3) a differentiable brush renderer that maps a stroke sequenceand physical brush parameters into a handwritten image, and 4) a zero-shot image refiner that refines rendered images via diffusion models.Extensive experiments and real-world robotic calligraphy demonstrationsvalidate our approach, achieving both structural and visual fidelity.

ECCV 2026

ReDesign: Recovering Editable Design Structures from Raster Images via Agentic Decomposition

Jooyeol Yun, Jintae Park, Hyesu Lim, Junha Hyung, Hyungjin Chung, Choo Jaegul

Recovering an editable design file from a raster image is a common and costly bottleneck in modern design workflows, yet remains challenging since editability depends on recovering multi-modal attributes, such as typography, vector geometry, colors, grouping, and layer ordering. We present ReDesign, an agentic framework that grows an editable layer hierarchy by selecting and composing specialized tools across modalities. To keep this long decision process reliable despite imperfect tool outputs, we introduce graceful verification at each expansion, which provides local accept, prune, or retry feedback that prevents error accumulation and avoids large scale reruns. To evaluate editability at scale, we introduce the Figma Edit Replay Benchmark, consisting of 909 raw Figma files and 14,796 controlled edit instructions that replay edits on reconstructed outputs. Across this benchmark and standard reconstruction metrics, ReDesign achieves strong visual fidelity while delivering the highest editability across layout, color, and text edits, outperforming layered decomposition baselines and serial tool use pipelines.

ECCV 2026

Complex-Valued 2D Gaussian Representation for Computer-Generated Holography

Yicheng Zhan, Xiangjun Gao, Long Quan, Kaan Akşit

Complex-valued Gaussian primitives have recently been ex-plored for representing holographic radiance _x001C_elds in 3D novel view syn-thesis. In this work, we extend this line of research to the hologramoptimization domain and propose a structured representation based oncomplex-valued 2D Gaussian primitives. Inspired by Gabor's theory,we show that our primitive attains the minimum space_x0015_frequency un-certainty and reduces the parameter search space by 5:1 compared toper-pixel parameterization. To enable end-to-end training, we develop adi_x001B_erentiable rasterizer for our representation, integrated with a GPU-optimized light propagation kernel in free space. Extensive experimentsshow that our method reduces VRAM usage by up to 30% and acceler-ates optimization by 50% over standard autodi_x001B_-based implementations,delivers up to 13 dB higher PSNR than prior Gaussian-based methods,and achieves up to 3200× faster rendering while maintaining reconstruc-tion quality on par with existing CGH approaches. For evaluation, weintroduce a conversion procedure that adapts our representation to prac-tical hologram formats, including smooth and random phase-only holo-grams. By reducing the hologram parameter search space, our representa-tion enables a more scalable hologram estimation in the next-generationcomputer-generated holography systems.

ECCV 2026

LooseControlVideo: Directorial Video Control using Spatial Blocking

Shariq Farooq Bhat, Kalyan Sunkavalli, Niloy Mitra

Precise 3D spatial orchestration in text-to-video generationremains a signi_x001C_cant challenge, particularly for multi-object scenes wheresemantic layout and temporal dynamics are often entangled. While exist-ing depth-conditioned models achieve good structural _x001C_delity, they neces-sitate dense, frame-accurate guidance that is labor-intensive to authorfor dynamic events involving deformable objects. We present LooseC-ontrolVideo (LCV), a framework that enables intuitive and expressivecontrol by using sparse, oriented 3D boxes as a _x0010_blocking_x0011_ proxy. Thisallows users to author high-level layout and trajectory while leveraginga video generative model to generate realistic occlusions, dynamics andinteractions. We achieve this by _x001C_ne-tuning a Wan 2.2 backbone on avideo dataset annotated with DNOCS, a novel encoding for 3D size, ori-entation and depth-ordered occlusions. Furthermore, our method allowsfor localized re_x001C_nement_x0016_such as adjusting a jump trajectory or addingan interaction_x0016_with minimal disruption to the global scene context. Ex-tensive evaluations on the nuScenes, HO-3D, and BEHAVE benchmarksdemonstrate that LCV signi_x001C_cantly outperforms existing 2D-box and_x001D_ow-based baselines. Our _x001C_ndings indicate a 1.2-3x improvement in Tra-jectory Error; 2x improvement in Rigid Motion Consistency; and a 1.5-2x increase in Occlusion Accuracy over current state-of-the-art layout-conditioned models, demonstrating that oriented 3D primitives providegood geometric prior for complex, multi-agent video authoring.

ECCV 2026

From Synchrony to Sequence: Exo-to-Ego Generation via Interpolation

Mohammad Mahdi, Nedko Savov, Danda Paudel, Luc Van Gool

Exo-to-Ego video generation aims to synthesize a first-personvideo from a synchronized third-person view and corresponding cam-era poses. While paired supervision is available, synchronized exo-egodata inherently introduces substantial spatio-temporal and geometricdiscontinuities, violating the smooth-motion assumptions of standardvideo generation benchmarks. We identify this synchronization-inducedjump as the central challenge and propose Syn2Seq-Forcing, a sequen-tial formulation that interpolates between the source and target videosto form a single continuous signal. By reframing Exo2Ego as sequentialsignal modeling rather than a conventional condition–output task, ourapproach enables diffusion-based sequence models, e.g. Diffusion Forc-ing Transformers (DFoT), to capture coherent transitions across framesmore effectively. Empirically, we show that interpolating only the videos,without performing pose interpolation already produces significant im-provements, emphasizing that the dominant difficulty arises from spatio-temporal discontinuities. Beyond immediate performance gains, this for-mulation establishes a general and flexible framework capable of uni-fying both Exo2Ego and Ego2Exo generation within a single contin-uous sequence model, providing a principled foundation for future re-search in cross-view video synthesis. The code will be released at https://github.com/insait-institute/Syn2Seq.

ECCV 2026

Beyond Pixel Mimicry: Disentangled Self-Similarity Rewards for Diverse Subject-Driven Generation

Qian Wang, Zhenyu Li, Abdelrahman Eldesokey, Peter Wonka

Subject-driven image generation faces an “Identity-DiversityParadox”, where strong identity preservation often leads to rigid and low-diversity outputs. We propose a post-training framework called DivRLthat jointly optimizes identity consistency and structural diversity si-multaneously by leveraging disentangled visual features from a robustsimilarity model. Specifically, we introduce a Negative Self-SimilarityMeasure (nSSM) to quantify structural diversity, and Visual Seman-tic Matching (VSM) to evaluate identity consistency. We propose an“Explore-and-Suppress” strategy that treats VSM as a gated constraint:the model freely explores structurally diverse configurations, and onlysamples that violate the identity threshold are penalized via a quadratichinge loss. This converts identity preservation from a competing objectiveinto a feasibility constraint, allowing nSSM and VSM to improve jointly.Experiments demonstrate that our method effectively pushes the modelto generate both consistent and diverse images and improves structuraldiversity while maintaining comparable identity consistency through agated optimization formulation.

ECCV 2026

FlexComposer: Unified Video Compositing from Images to Dynamic Footage with Flexible Trajectory Control

Songchun Zhang, Sitong Guo, Xianghao Kong, Pengwei Liu, Yuwei Guo, Lvmin Zhang, Anyi Rao

Generative video compositing, which involves inserting ex-ternal assets seamlessly into existing video sequences, is essential forcontent creation and visual effects. However, existing approaches suf-fer from a control-fidelity trade-off: they either hallucinate motion fromstatic images, failing to preserve the dynamics of pre-animated assets, orlack fine-grained spatial control for precise asset placement along user-defined trajectories. We propose FlexComposer, a unified framework thatstandardizes video compositing as a trajectory-guided conditional gen-eration task, enabling the seamless integration of both static images anddynamic footage. Our approach introduces three key designs: (1) a Uni-fied Canonical Foreground Representation that decouples an object’s in-trinsic motion from its global displacement, standardizing heterogeneousinputs into a stabilized, centered latent space; (2) a Spatial-Aware La-tent Injection strategy that exploits the translation equivariance of VAElatent spaces to transport canonical features onto target trajectories viaa parameter-free mechanism; and (3) a Hybrid Dataset and Synthetic-to-Real Curriculum that synergizes procedural simulation, real-world cine-matic footage, and generative data to implicitly learn physically plausibleillumination and shadow harmonization. This unified design handles di-verse inputs—from product photos to dynamic subjects—achieving high-fidelity motion control and environmental integration without the needfor explicit 3D reconstruction or auxiliary learnable adapters. Extensiveexperiments demonstrate that FlexComposer outperforms state-of-the-art methods in visual quality, temporal consistency, and trajectory ad-herence.

ECCV 2026

Learning Implicit Constitutive Laws for Dynamic 3D Gaussian Splatting from Monocular Videos

Xiaoyang Liu, Kai Han

We present GCA (Gaussian Constitutive Alignment), a frame-work for learning implicit constitutive laws from monocular dynamicvideo of deformable objects represented by 3D Gaussians. Given a staticmulti-view scan for geometric initialization, our method learns intrin-sic physical dynamics solely from a single fixed-viewpoint video of themoving object. Existing implicit methods often suffer from local minimaunder noisy supervision and lack physical interpretability, while explicitapproaches rely on predefined constitutive equations, limiting generaliz-ability and becoming unstable in monocular settings. To address thesechallenges, our framework unifies LoRA-based adaptation with two keyalignment modules. First, we propose Rank-based Depth-Geometric An-chors (RDGA) to establish robust geometric constraints from monoc-ular dynamic observations via scale-invariant rank-based depth align-ment, reducing the reliance on unreliable pixel-level color supervision.Second, a Constitutive Prior Regularizer (CPR) integrates classical con-stitutive models as soft differentiable priors, regularizing the optimiza-tion while preserving the flexibility of implicit modeling—even when theactual material is absent from the hypotheses. Extensive experiments onsynthetic, real-to-sim, and real-world datasets demonstrate that GCAoutperforms existing methods, achieving 48% lower Chamfer Distancethan the strongest baseline on synthetic benchmarks while remainingrobust under monocular supervision.

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