paperlist.ai

AI conference papers

AI conference paper library

Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

ECCV 2026

HuCollisionField: Resolving Self-Collisions via Neural Fields for Human Prediction

Zhengyuan Li, Zeyun Deng, Yifan Shen, Liang-Yan Gui, Miaolan Xie, Joseph Campbell, Xifeng Gao, Kui Wu, zherong pan, Aniket Bera

Self-collision remains a persistent challenge in SMPL-basedhuman pose estimation and motion generation. Under extreme articula-tions or stochastic motion synthesis, generated meshes frequently exhibitself-penetrations, leading to physically implausible results. We proposePoseShield, a neural collision constraint defined directly in SMPL posespace. We formulate collision correction as a constrained optimizationproblem and connect the learned constraint with the Eikonal equation.Enforcing Eikonal regularization ensures non-vanishing gradients nearthe collision boundary, improving numerical stability and robustness ofthe optimization process. Unlike prior methods that operate in the meshspace or rely on heuristic penalties, our approach operates directly in thelow-dimensional space of human poses and is theoretically grounded. Thesame learned constraint extends to human motion sequences, providinga generator-agnostic post-hoc collision corrector without retraining theunderlying motion model. Experiments on a newly constructed SMPLpose benchmark show that our method achieves a 95.8% success rateand outperforms state-of-the-art baselines.

ECCV 2026

Semantic Line Diffusion: Character-Consistent Line Art from text-annotated Storyboards

Seo-Yeon Choi, Kyungsu Lee

Generating clean line art from conti, rough storyboard sketchesused in webtoon production, remains a labor-intensive process requiringsubstantial artistic expertise. Conti sketches provide only coarse geom-etry and sparse semantic cues, making it difficult to recover detailedline art while preserving consistent character identity across sequentialpanels. Existing sketch-to-image or diffusion-based translation modelsprocess images independently and therefore struggle to maintain cross-panel identity coherence. We present PanelDiff, a diffusion transformerframework for conti-to-line-art generation that explicitly models cross-panel character consistency. PanelDiff integrates three components. Amulti-reference character conditioning module encodes character labelsand multiple line-art exemplars into compact identity tokens for robustidentity guidance under ambiguous conti inputs. A memory-augmentedrepresentation bank accumulates character-aware features from previ-ously generated panels, while a similarity interpreter dynamically re-trieves relevant entries for the current panel. A panel-aware diffusiontransformer then jointly attends to conti structure, textual descriptions,identity tokens, and retrieved memory features to produce coherent line-art sequences. To support this task, we construct a conti–line-art datasetwith character identity annotations and sequential panel structures. Ex-periments show that PanelDiff improves line-art fidelity, identity preser-vation, and cross-panel consistency over strong baselines, and ablationstudies verify the contribution of each component. The code and datasetare available online (huggingface) ⋆ .

ECCV 2026

How to Teach Large Multimodal Models New Skills

Zhen Zhu, Yiming Gong, Yao Xiao, Yaoyao Liu, Derek Hoiem

How can we teach large multimodal models (LMMs) newskills without erasing prior abilities? We study sequential fine-tuningon five target skills while monitoring general ability on eight held-outbenchmarks across three model families. Surprisingly, we find that per-formance lost on held-out tasks after fine-tuning on one skill can partlyrecover when the model is subsequently tuned on a different skill. Wetrace this behavior to a measurable shift in the output token distribu-tion, manifested through a simple counting-bias probe that shows theshift co-varies with forgetting. Guided by this insight, we identify twosimple, robust tuning recipes that learn strongly while limiting drift: (i)updating only the self-attention projection layers (SA Proj., ∆ learning+24.9 / ∆ held-out forgetting -0.6), and (ii) updating only the MLPGate&Up while freezing the Down projection (+30.5 / -2.1). Both sub-stantially outperform full-LLM tuning (+31.8 / -23.3) in the learning–forgetting trade-off. We also compare against common forgetting mitiga-tion methods—Learning without Forgetting (LwF), LoRA, Mixture-of-Experts, and weight-space interpolation (WiSE-FT)—and find that ourselective tuning recipes match or exceed their learning–stability balancewhile remaining simpler, requiring no replay, auxiliary parameters, orper-stage tuning. These results hold across LLaVA-OneVision, LLaVA-NeXT, and Qwen2.5-VL, confirming that the key to teaching LMMs newskills without forgetting lies in controlling output distribution shift bychoosing which components to tune. Code will be made available.

ECCV 2026

QST-SAM: Leveraging Cross-modal Instructions for Few-shot Referring Video Object Segmentation

Xin Liu, Weijia Li, Tao Chen, Hongsong Wang, Guosen Xie, Caifeng Shan, Fang Zhao

The advancement of Video Object Segmentation (VOS) isimpeded by its heavy reliance on expensive annotations. This has led toa landscape of compromised alternatives: unsupervised methods provideweak information, few-shot learners are plagued by referential ambiguityfrom distractors, and referring techniques are limited by a lack of visionpriors. We revisit Few-Shot Referring Video Object Segmentation (FSR-VOS) under a streamlined and practical formulation that segments tar-get objects using only query-level textual descriptions and a small set ofannotated support images. This dual conditioning enables coarse-to-finedisambiguation and precise boundary refinement with minimal annota-tion cost. To address the challenge of complex multi-source informationfusion, we propose the QST-SAM framework. Specifically, a SupportInstruction Compressor is introduced to refine and condense the sup-port information into compact representations. The Q-S-T Transformerfurther facilitates comprehensive integration of multi-modal supervisorycues from query, support, and text branches. The Multi-Priors Prompt-ing module provides the SAM2 prompt encoder with enriched guidancefor precise, context-aware segmentation. We further establish a new FS-RVOS benchmark derived from Ref-Youtube-VOS and Ref-DAVIS17, onwhich QST-SAM achieves state-of-the-art performance, validating its ro-bustness and effectiveness in segmentation.

ECCV 2026

RayRoPE: Projective Ray Positional Encoding for Multi-view Attention

Yu Wu, Minsik Jeon, Rick Chang, Oncel Tuzel, Shubham Tulsiani

We study positional encodings for multi-view transformersthat process tokens from a set of posed input images, and seek a mech-anism that encodes patches uniquely, allows SE(3)-invariant attentionwith multi-frequency similarity, and can adapt to the geometry of theunderlying 3D scene. We find that prior (absolute or relative) encod-ing schemes for multi-view attention do not meet these desiderata, andpresent RayRoPE to address this gap. RayRoPE represents patch po-sitions based on associated rays and computes query-frame projectivecoordinates to ensure SE(3) invariance. To adapt to scene geometry,RayRoPE predicts (without direct supervision) a per-token depth to ob-tain its position along the corresponding ray, while also modeling uncer-tainty and analytically computing the expected positional encoding. Wevalidate our method on the tasks of novel-view synthesis, stereo depthestimation, and feed-forward 3DGS reconstruction. While remaining ef-ficient, RayRoPE consistently improves over alternate position encodingschemes (e.g. 24% relative improvement on LPIPS in RE10K).

ECCV 2026

HART: High-Resolution Annotation-Free Reasoning Technique through a Closed-loop Framework

Jiacheng Yang, Anqi Chen, Yunkai Dang, Qi Fan, Cong Wang, Wenbin Li, Feng Miao, Yang Gao

Current Large Multimodal Models (LMMs) struggle withhigh-resolution visual inputs during the reasoning process, as the num-ber of image tokens increases quadratically with resolution, introducingsubstantial redundancy and irrelevant information. A common practiceis to identify key image regions and refer to their high-resolution counter-parts during reasoning, typically trained with external visual supervision.However, such visual supervision cues require costly grounding labelsfrom human annotators. Meanwhile, it remains an open question how toenhance a model’s grounding abilities to support reasoning without rely-ing on additional annotations. In this paper, we propose High-resolutionAnnotation-free Reasoning Technique (HART), a closed-loop frameworkthat enables LMMs to focus on and self-verify key regions of high-resolution visual inputs. HART incorporates a post-training paradigmin which we design Advantage Preference Group Relative Policy Opti-mization (AP-GRPO) to encourage accurate localization of key regionswithout external visual annotations. Notably, HART provides explain-able reasoning pathways and enables efficient optimization of localiza-tion. Extensive experiments on MME-RealWorld-Lite, TreeBench, V*Bench, HR-Bench-4K/8K, and MMStar demonstrate that HART im-proves performance across a wide range of high-resolution visual tasks,consistently outperforming strong baselines. Code will be available athttps://github.com/RL-MIND/HART.

ECCV 2026

GRE-Diff: Gaussian Room Embeddings for Structured Layout Diffusion

Jing Wang, Haoran Xiong, Zihao Yan, Minglun Gong, Hui Huang

Designing functional and aesthetically coherent floor plansrequires exploring a vast space of possible room arrangements, a taskthat quickly becomes overwhelming for human designers. In this pa-per, we propose GRE-Diff, a controllable and interactive diffusion-basedframework that automates the creation and editing of apartment floorplans under user-specified constraints. By combining AI-generated sug-gestions with real-time, human-in-the-loop editing, the system enablesusers to specify room types, room counts, boundary shapes, and editingoperations through LLM-parsed instructions or GUI-based interaction.It then generates a diverse set of plausible and well-structured designs forrefinement. At the core of our approach is Gaussian Room Embedding(GRE), a continuous latent representation that models each room as aspatial Gaussian distribution capturing its location and extent. Exten-sive experiments on the RPLAN dataset show that GRE-Diff produceshigh-quality, constraint-aware, and editable polygonal layouts, offeringa practical step toward bridging AI-driven automation and human cre-ativity in spatial design.

ECCV 2026

Bridging Visual Representation and Reinforcement Learning from Verifiable Rewards in Large Vision-Language Models

Yuhang Han, Yuyang Wu, Zhengbo Jiao, Yiyu Wang, Xuyang Liu, Shaobo Wang, Hanlin Xu, Xuming Hu, Linfeng Zhang

Reinforcement Learning from Verifiable Rewards (RLVR)has substantially enhanced the reasoning capabilities of large languagemodels in abstract reasoning tasks. However, its application to LargeVision-Language Models (LVLMs) remains constrained by a structuralrepresentational bottleneck. Existing approaches generally lack explicitmodeling and effective utilization of visual information, preventing vi-sual representations from being tightly coupled with the reinforcementlearning optimization process and thereby limiting further improvementsin multimodal reasoning performance. To address this limitation, wepropose KAWHI (Key-Region Aligned Weighted Harmonic Incentive),a plug-and-play reward reweighting mechanism that explicitly incorpo-rates structured visual information into uniform reward policy optimiza-tion methods (e.g., GRPO and GSPO). The method adaptively localizessemantically salient regions through hierarchical geometric aggregation,identifies vision-critical attention heads via structured attribution, andperforms paragraph-level credit reallocation to align spatial visual ev-idence with semantically decisive reasoning steps. Extensive empiricalevaluations on diverse reasoning benchmarks substantiate KAWHI as ageneral-purpose enhancement module, consistently improving the perfor-mance of various uniform reward optimization methods. Code is availableat https://github.com/kawhiiiileo/KAWHI.

ECCV 2026

GraphCPD: Coherent Point Drift for Point Cloud Registration via Graph Signal Processing

Yingcheng Lai, Xingjian Wang, Li Chai

Probabilistic point cloud registration has attracted increasing attention due to its robustness to noise, outliers and occlusions. However, existing methods often suffer from high computational cost and neglect the role of informative priors. In this paper, we propose a new probabilistic registration method based on graph signal processing (GSP), called graph coherent point drift (GraphCPD). Specifically, we design a high-pass graph filter to extract high-frequency components, which are theoretically proven to be invariant under rigid transformations. These components are combined with point coordinates and normals to form a high-dimensional graph signal. We construct a local graph based on the graph signal and use the graph Laplacian model for registration. Compared with the classical Gaussian mixture models (GMMs), graph Laplacian models provide more discriminative geometric representations and enhance the model’s ability to capture graph structure. Furthermore, we exploit the invariance of high-frequency components to define prior probabilities, significantly reducing the corresponding search space and improving the speed of registration. Experimental results demonstrate that our method improves runtime efficiency over most existing probabilistic methods, while maintaining competitive registration accuracy, especially on large-scale point clouds. The source code is available at https://github.com/YCLai1997/GraphCPD.

ECCV 2026

FUSE: Filter-Free Unified Spatiotemporal Estimation of SpO2 via Wave-Transport Modeling

Shahzad Ahmad, NUVVURU REDDY, Ram Padhy, Sukalpa Chanda, Umapada Pal

Blood oxygen saturation (SpO2 ) is a vital indicator of respi-ratory and cardiovascular health, increasingly targeted by camera-based,contactless monitoring systems. Existing video-based approaches typi-cally rely on predefined frequency filtering to separate pulsatile (AC)and baseline (DC) components. Such spectral decomposition propagatesband-limited noise and assumes signal stationarity, limiting robustnessunder motion, illumination variation, and subject diversity.We propose FUSE, a filter-free, physics-guided framework that learnsAC and DC representations via structured differential constraints ratherthan static spectral filtering. The pulsatile component is regularized as aband-limited harmonic field through a second-order ordinary differentialequation, while spatial coherence is enforced by modeling it as a trans-ported density field under an advection-based partial differential con-straint. This coupled wave-transport formulation enforces physiologicallyconsistent spatiotemporal dynamics without explicit frequency-domain

ECCV 2026

From Predictions to Embeddings: Dual Knowledge Distillation for Instance-Dependent Partial Label Learning

Lilong Duan, Ke Wang, Yao Zhang, Jun Tang

Partial label learning in the instance-dependent setting (ID-PLL) poses significant challenges, as candidate labels are highly corre-lated with sample features, and strong semantic overlap among candi-dates often leads to error-prone disambiguation. Meanwhile, the infor-mative dark knowledge inherent in non-candidate labels remains largelyunderexplored. To address these issues, we propose DKDP, a dual dis-tillation method that extracts and transfers more stable and reliableknowledge from the teacher model. Specifically, we first use constrainedprojection to structurally rectify the teacher’s prediction distribution,enforcing the prior that the minimum value of candidate labels exceedsthe maximum value of non-candidate labels. On this basis, we guide thestudent model to fit the corrected teacher outputs. We further derive theminimum modification required to satisfy the prior, which theoreticallyguarantees the interpretability of the rectification process. Moreover, weintroduce class prototypes to model the distribution of fine-grained se-mantic relations, extending reliable knowledge distillation from the out-put space to the embedding space. This yields a stable, separable geomet-ric structure in the feature space. Extensive experiments on benchmarkdatasets demonstrate the effectiveness of our proposed method.

ECCV 2026

ExpoMotion: A Large-Scale Benchmark and A Householder Projection Network for Multi-Exposure Fusion

Yao Liu, Lishen Qu, Jie Liang, Shihao Zhou, Hui Zeng, Yabin Peng, Huipeng Lin, Lei Zhang, Jufeng Yang

Multi-Exposure Fusion (MEF) effectively extends dynamicrange, but practical deployment is hindered by motion-induced ghostingand the scarcity of high-quality dynamic benchmarks. Current bench-marks largely neglect dynamic scenes and lack reliable ground truth,making it difficult to handle the complexity of real-world motions. In re-sponse, we introduce ExpoMotion, a large-scale benchmark designed toevaluate deghosting capabilities. Comprising 1,738 sequences and 10,909images across diverse environments, it covers a wide range of motionsand provides high-fidelity GTs constructed through an expert-guided ac-quisition pipeline. To tackle the complex dynamics and extreme con-ditions captured in this benchmark, we propose the Householder Or-thogonal Projection network (HOP), which revisits MEF deghostingfrom a mathematical perspective via Householder transformation, de-coupling multi-frame alignment into exposure pre-alignment and ghostfiltering. Specifically, the Global Priors Illumination Alignment (GPIA)module first rectifies drastic dynamic range discrepancies by utilizingglobal statistics for exposure harmonization. Regarding ghost removal,our Householder Orthogonal Attention (HOA) models artifacts as or-thogonal perturbations. By employing a dynamic Householder reflector,HOA effectively projects ghosts out of the feature manifold while pre-serving high-frequency details. Experiments demonstrate that our Ex-poMotion dataset enables superior generalization and artifact-free detailrestoration, while also validating the effectiveness and efficiency of theHOP method. The dataset and code are available at https://github.com/Leo-LiuYao/ExpoMotion.

ECCV 2026

EatVid-Bench: A Multimodal Fine-Grained Eating Behavior Video Dataset

Xiangpeng Zheng, Zhenbo Xu, Gong Huang, Zhu Li, Qinghong Yang

Understanding fine-grained eating behaviors in unconstraineddaily-life videos is essential for dietary monitoring and behavioral healthassessment, yet existing video-language benchmarks lack the temporalgranularity and domain-specific grounding required to rigorously evalu-ate models in this health-critical domain. We introduce EatVid-Bench,a large-scale, multi-dimensional benchmark designed to advance fine-grained eating behavior understanding. Our dataset comprises nearly700 real-world eating sessions (around 3,000 minutes) annotated througha novel three-tier automated pipeline that integrates 12 complementarysignals, including body pose, food detection, bite events, and facial ex-pressions, with each annotation traceable to its source signal for fullinterpretability and verifiability. Building on this foundation, we con-struct a question-answering benchmark spanning seven capability di-mensions, three difficulty levels, and five question types, with rigorousprovenance tracking throughout. Comprehensive evaluation of state-of-the-art Video-LLMs exposes a substantial perception–reasoning gap, par-ticularly in sub-second temporal grounding and cross-frame counting. Tofurther validate the effectiveness of EatVid-Bench, we propose a domain-adapted fine-tuning strategy to provide a strong open-source baselineby leveraging structured training annotations as explicit reasoning su-pervision. Code and benchmark are publicly available at GitHub andHuggingFace.

ECCV 2026

Dual-End Consistency Model

Linwei Dong, Ruoyu Guo, Ge Bai, Zehuan Yuan, Yawei Luo, Changqing Zou

The slow iterative sampling nature remains a major bottle-neck for the practical deployment of diffusion and flow-based genera-tive models. While consistency models (CMs) represent a state-of-the-art distillation-based approach for efficient generation, their large-scaleapplication is still limited by two key issues: training instability andinflexible sampling. Existing methods seek to mitigate these problemsthrough architectural adjustments or regularized objectives, yet over-look the critical reliance on trajectory selection. In this work, we firstconduct an analysis on these two limitations: training instability orig-inates from loss divergence induced by unstable self-supervised term,whereas sampling inflexibility arises from error accumulation. Based onthese insights and analysis, we propose the Dual-End ConsistencyModel (DE-CM) that selects vital sub-trajectory clusters to achievestable and effective training. DE-CM decomposes the PF-ODE trajec-tory and selects three critical sub-trajectories as optimization targets.Specifically, our approach leverages continuous-time CMs objectives toachieve few-step distillation and utilizes flow matching as a boundaryregularizer to stabilize the training process. Furthermore, we propose anovel noise-to-noisy (N2N) mapping that can map noise to any point,thereby alleviating the error accumulation in the first step. Extensiveexperimental results show the effectiveness of our method: it achieves astate-of-the-art FID score of 1.70 in one-step generation on the ImageNet256×256 dataset, outperforming existing CM-based one-step approaches.

ECCV 2026

Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout

Haozhuang Chi, Daosheng Qiu, Hao Su, Haochen Liu, Zirui Li, Haoruo Zhang, Chen Lv

Safe L2/L3 driving automation requires anticipating human-in-the-loop reactions during shared-control transitions. While most driv-ing world models forecast the external environment, in-cabin intelligenceremains strictly recognition-oriented and lacks multi-step rollout capa-bilities for driver dynamics. We introduce Driver-WM, a driver-centriclatent world model that rolls out in-cabin dynamics causally conditionedon out-cabin traffic context. This formulation unifies physical kinemat-ics forecasting with auxiliary behavioral and emotional semantic recog-nition. Operating in a compact latent space constructed from frozenvision-language features, Driver-WM adopts a dual-stream architectureto separately encode external traffic and internal driver states. Thesestreams are directionally coupled via a gated causal injection mechanism,which uses a learned vector gate to modulate external contextual per-turbations while strictly enforcing temporal causality. Experiments onAIDE show robust long-horizon forecasting on reactive high-motion clips,improved driver/traffic semantic alignment, and controlled interventionsthat expose the external-to-internal mechanism.

ECCV 2026

DriveFine: Refining-Augmented Masked Diffusion VLA for Accurate and Robust Driving

Chenxu Dang, Sining Ang, Yongkang Li, Haochen Tian, Jie Wang, Guang Li, Hangjun Ye, Jie Ma, Long Chen, Yan Wang

End-to-end autonomous driving increasingly relies on gener-ative planners, broadly classified into continuous diffusion policies anddiscrete token-based vision-language-action models (VLAs). While diffu-sion policies exploit parallel iterative noise prediction to efficiently gener-ate accurate trajectories, they suffer from cross-modal misalignment, lim-ited training efficiency, and poor generalization. Conversely, token-basedVLAs generate discrete trajectory tokens autoregressively, offering bettergeneralization but being constrained by inefficient causal reasoning, cu-mulative error, and irreversible decoding. To overcome these limitations,we propose DriveFine, a vision-language-action model that exploresmasked diffusion LLMs for trajectory planning, which significantly im-proves the efficiency, flexibility, and adaptability of trajectory generation.To address the inherent irreversibility of token decoding and improve thetrajectory quality, we introduce a plug-and-play block Mixture-of-Expert(block MoE) module, which seamlessly injects the refinement capa-bility into the dLLM at minimal cost. Through explicit expert routingand gradient isolation, DriveFine decouples generation and refinement,preventing cross-task interference. We further devise a tailored hybridreinforcement learning strategy to facilitate the effective exploration ofthe refinement expert and further explore the performance ceiling. Ex-tensive experiments on the NAVSIM v1, v2, and Navhard benchmarksdemonstrate that DriveFine exhibits strong performance, robustness andefficiency. The code will be released.

ECCV 2026

Do Flat Minima Improve Sparse Novel View Synthesis?

Youngsik Yun, Dongjun Gu, Youngjung Uh

Despite the success of recent novel view synthesis methods,they tend to struggle in sparse-view settings. This poor generalizationto unseen viewpoints is an inherent challenge when training with lim-ited data. To address this, we investigate the relationship between losssharpness and generalization in novel view synthesis—an underexploreddirection. Interestingly, while pursuing flatter minima is widely knownto improve generalization in deep learning, reducing loss sharpness is notalways beneficial in novel view synthesis. We demonstrate that this dif-ference arises because high-detail regions inherently require a sharp losslandscape for accurate reconstruction, whereas low-detail regions benefitfrom a flat loss landscape for improving generalization. Based on this in-sight, we introduce structure-aware sharpness, defined within structure-adaptive neighborhoods, and propose to adaptively adjust the sharpnessregularization weight according to the local image structure. This strat-egy encourages flatter minima for generalization while preserving the losssharpness necessary to reconstruct fine details. Across various datasetsand configurations, our strategy consistently improves a wide range ofbaselines. Code is available at https://bbangsik13.github.io/FASR.

ECCV 2026

DASAM3D: A Unified Foundation Model for Enhanced 3D Scene Reconstruction and Segmentation

RONNY XAVIER VELASTEGUI SANDOVAL, Max Pfingsthorn, Sezer Karaoglu, Theo Gevers

We present DASAM3D, a framework that fuses the complementary strengths of SAM3D and Depth Anything 3 (DA3) for objectaware 3D scene reconstruction. A fundamental appearance–geometry trade-off exists: SAM3D yields geometrically coherent per-object Gaussian primitives but with unrealistic appearance, while DA3 delivers photorealistic 3DGS reconstructions yet lacks object-level geometric reasoning. Our key insight is to use SAM3D exclusively as a 3D geometric layout provider, guiding DA3 geometry refinement while preserving its photorealistic textures. DASAM3D proceeds through three differentiable stages—(1) Global Alignment Optimization, (2) Per-Object Alignment Refinement, and (3) Layout-Based Geometry Optimization—and inherits DA3’s flexibility for posed or pose-free inputs at any scale. Experiments on DL3DV-10K and MipNeRF-360 show consistent gains in reconstruction accuracy and novel view synthesis; a user-preference study confirms DASAM3D’s object renderings are preferred over SAM3D’s in visual fidelity.

ECCV 2026

CustomX: Unified Character, Action, and Scene Customization in Video World Models

Yitong Wang, Fangyun Wei, Hongyang Zhang, Bo Dai, Yan Lu

Recent advances in world models have greatly enhanced in-teractive environment simulation. Existing methods mainly fall into two⋆ Equal contribution. † Corresponding author.categories: (1) static world generation models, which construct 3D en-vironments without active agents, and (2) controllable-entity models,which allow a single entity to perform limited actions in an otherwise un-controllable environment. In this work, we introduce CustomX, leverag-ing the realism and structural grounding of static world generation whileextending controllable-entity models to support user-specified characterscapable of performing open-ended actions. Users can provide a 3DGSscene and a character, then use natural language to direct the characterto perform diverse behaviors, ranging from basic locomotion to object-centric interactions, while freely exploring the environment. CustomXsynthesizes temporally coherent video clips that preserve visual fidelitywith the provided scene and character, formulated as a conditional au-toregressive video generation problem. Built upon a pre-trained videogenerator, our training strategy significantly enhances motion dynamicswhile maintaining generalization across actions and characters. Our eval-uation covers a broad range of aspects, including visual quality, characterconsistency, action controllability, and long-horizon coherence.

ECCV 2026

Curvature-Adaptive Consistency Flow Matching: Autonomous Trajectory Optimization via Reinforcement Learning

Songtao Tian, Guhan Chen, Bohan Li, Jingyi Ma, Zixiong Yu

Consistency distillation has significantly accelerated diffusion-model inference, but its sampling dynamics remain underexplored. Wereveal an asymmetry: although Logit-Normal sampling priors work well forstandard iterative generation, consistency distillation exhibits a differentdifficulty profile (e.g., U-shaped), with optimization bottlenecks concen-trated at the boundary stages rather than intermediate steps. To addressthe limitations of static sampling under evolving learning demands, wepropose Curvature-Adaptive Consistency Flow Matching (CACFM). Byformulating distillation as a dynamic decision process, CACFM usesa lightweight reinforcement learning agent to probe Probability FlowODE trajectories and construct an efficiency-oriented curriculum thatprioritizes critical regions without manual scheduling. Combined withFlow-adapted DMD and adversarial consistency objectives, our RL-basedscheduler achieves state-of-the-art results on large-scale models suchas FLUX and SDXL, mitigating structural deformities and preservinghigh-frequency details in extreme few-step regimes.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.