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ECCV 2026

BiCE-HG: A Bi-Conditional Egocentric Hand Gesture Dataset for Intelligent Reality Systems

Awfa Dakheel, Charith Abhayaratne

Robust egocentric hand-gesture recognition remains chal-lenging under the rapidly changing illumination typical of intelligent re-ality environments. Existing first-person gesture datasets rarely quantifylighting, limiting controlled analysis of illumination robustness. Unlikeprior egocentric datasets that describe lighting qualitatively (e.g., “in-door”, “bright”), BiCE-HG is the first to provide calibrated lux measure-ments at 80 spatial points, enabling reproducible illumination robustnessbenchmarking. BiCE-HG (Bi-Conditional Egocentric Hand Gestures)dataset addresses this gap as the first egocentric gesture dataset withmeasured bi-conditional lighting and spatial illuminance documentation.BiCE-HG contains 19 gestures performed by 23 participants across sixconditions combining sitting, standing, and walking with full and lowlighting setups. Walking trials capture realistic motion through a 4 mlighting corridor with transitions between bright and dim zones. Spatialilluminance is recorded every 25 cm using a calibrated meter, enablingprecise modelling of lighting gradients and reproducible experimentalconditions. Baseline evaluation reaches 81.24% validation accuracy (F1= 0.80) on the static (single-frame) release with a no-attention ST-GCN,the dynamic (temporal) release lifts accuracy to 88.57% with the identicalarchitecture, isolating a 7.3-percentage-point gain from temporal infor-mation alone, while adding tri-attention contributes a further marginalgain to 89.24% (F1 = 0.88). Lighting-robustness analysis shows stablespatial measurements, with a coefficient of variation below 5% for mostconditions across a 4.95× illuminance range (29-523 lux). BiCE-HG pro-vides quantified environmental metadata and realistic mobility lightinginteractions, supporting the development of illumination-robust egocen-tric gesture-recognition systems for AR/VR applications.The dataset ispublicly available at https://doi.org/10.15131/shef.data.32374137

ECCV 2026

BeTTER: Diagnose the Illusion of Embodied Reasoning in Vision-Language-Action Models

Haiweng Xu, Sipeng Zheng, Hao Luo, Wanpeng Zhang, Zongqing Lu

Recent Vision-Language-Action (VLA) models report im-pressive success rates on robotic benchmarks, but whether these scoresreflect genuine embodied reasoning remains questionable. To addressthis gap, we introduce BeTTER, a diagnostic Benchmark for TestingTrue Embodied Reasoning. By applying targeted causal interventions(e.g., spatial layout shifts and temporal extrapolation) under strict kine-matic isolation, BeTTER decouples high-level cognitive failures fromlow-level execution errors. Systematic evaluations reveal that state-of-the-art VLAs break down under these interventions, exhibiting lexical-kinematic shortcuts, semantic feature collapse, behavioral inertia, andcausal state-tracking failures. Our analysis traces these failures to twocoupled sources: real-time deployment constraints, such as capacity com-pression and myopic perception, which weaken semantic representations,and behavioral cloning, which can amplify predictive but non-causal cor-relations into shortcut policies under static training distributions. Wefurther show that highly static evaluations mask these defects by per-mitting overfitting to sensorimotor priors. Real-world robotic validationconfirms that these failures are not simulation artifacts, highlighting theneed for future VLA paradigms to preserve semantic and causal reason-ing while maintaining efficient continuous control. Code and benchmark:https://github.com/BeingBeyond/BeTTER.

ECCV 2026

ART-VSR: Adaptive Rectified Trajectories for One-Step Video Super-Resolution

Jianhui Zhang, Chen Fang, Wanghao Wanghao, Chaoyu Feng, Lei Lei, Jue Wang, Shuaicheng Liu

Generative priors have advanced VSR, yet their multi-step inference remains costly. Recent trajectory-based one-step VSR methods accelerate generation from low-resolution (LR) inputs, but can struggle under complex real-world degradations. We analyze this limitation as trajectory drift, caused by initial state mismatch and a global timestep constraint. We propose ART-VSR, a state-time adaptation framework that estimates a continuous token-wise timestep map with an Adaptive Timestep Estimator (ATE) and uses it to guide a Latent Trajectory Rectifier (LTR) toward a compatible starting state. Conditioned on the rectified latent and spatial timestep map, ARTVSR achieves highly competitive one-step VSR performance, producing crisp, artifact-reduced details while preserving temporal consistency. Code: https://github.com/Roveer/ART_VSR.

ECCV 2026

AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting

Minh-Quan Bui, Jaeho Moon, Munchurl Kim

While 3D Vision Foundation Models (3DVFMs) have demon-strated remarkable zero-shot capabilities in visual geometry estimation,their direct application to generalizable novel view synthesis (NVS) re-mains challenging. In this paper, we propose AirSplat, a novel train-ing framework that effectively adapts the robust geometric priors of3DVFMs into high-fidelity, pose-free NVS. Our approach introduces twokey technical contributions: (1) Self-Consistent Pose Alignment (SCPA),a training-time feedback loop that ensures pixel-aligned supervision toresolve pose-geometry discrepancy; and (2) Rating-based Opacity Match-ing (ROM), which leverages the local 3D geometry consistency knowl-edge from a sparse-view NVS teacher model to filter out degraded prim-itives. Experimental results on large-scale benchmarks demonstrate thatour method significantly outperforms state-of-the-art pose-free NVS ap-proaches in reconstruction quality. Our AirSplat highlights the potentialof adapting 3DVFMs to enable simultaneous visual geometry estimationand high-quality view synthesis.

ECCV 2026

AdaptiveSplat: Texture Aware Controllable 3D Gaussian Allocation for Feed-Forward Reconstruction

Badrinath Singhal, Srihari K G, Sreehari Iyer, Ankit Dhiman, Venkatesh Babu Radhakrishnan

Current feed-forward 3D reconstruction methods predict pixelaligned Gaussian primitives, resulting in highly redundant representa-tions. A natural solution is to prune the redundant Gaussians, but naivepruning introduces severe artifacts and often requires inference time fine-tuning, breaking the feed-forward paradigm. Based on previous works,high frequency regions require more Gaussian primitives, while low fre-quency regions can be represented with significantly fewer primitives.Motivated by this, we propose a novel approach to explicitly controlthe number of Gaussians by leveraging local texture information. Ourapproach achieves this through three key components: (1) texture es-timation to capture spatial variation in scene detail, (2) texture-awarepruning that removes redundant Gaussians from low frequency regions,and (3) an adaptive Gaussian head that predicts the modified attributesof the retained primitives without breaking the feed-forward paradigm.Experiments on RE10K, ACID, DL3DV, Tanks and Temples, and DTUdemonstrate the effectiveness of our approach, while ablation studiesvalidate the contributions of its key components.

ECCV 2026

340 FPS Reflection-free Video from Spikes Modulated by a Rapidly Rotating Polarizer

Lishuai Huang, Yeliduosi Xiaokaiti, Youwei Lyu, Langyue Chang, Boxin Shi, Shikui Wei, Yao Zhao, Meng Jian, Yashen Wang, Yakun Chang

Removing reflections from mixed light using polarization of-fers the benefits of robust physical constraints. However, extending thisto high-speed scenes presents challenges due to the discrete frames ofconventional cameras. In this paper, we propose 340 FPS reflection-freevideo reconstruction by leveraging the high-speed spike camera modu-lated by a rapidly rotating polarizer. The core motivation of our approachrelies on exploiting the continuous and asynchronous firing of the spikesensor to densely capture instantaneous polarization states. However, theinherently low signal-to-noise ratio of spikes under extremely short inte-gration windows and light attenuation causes severe numerical instabilityin physical layer separation. To address this, we introduce a physically-driven framework featuring a differentiable Reliability-Weighted PhysicsSolver (RWPS). RWPS robustly separates reflection and transmissionlayers by dynamically adjusting learned confidence weights to handlesevere shot noise. Furthermore, we develop a refinement module thatleverages back-projection residuals and physical-discrepancy cues to ac-curately restore high-frequency textures. Extensive experiments demon-strate that our method enables robust reconstruction of high-fidelity andreflection-free videos.

ECCV 2026

Φeat: Physically-Grounded Material Feature Representation

Giuseppe Vecchio, Adrien Kaiser, Claudia Cuttano, ROUFFET Romain, Rosalie MARTIN, Elena Garces, Tamy Boubekeur

While foundation models have emerged as general-purposevisual backbones, their representations are primarily optimized for se-mantics and lack explicit modeling of physical factors, such as reflectance,hindering their efficacy in tasks requiring explicit material reasoning. Weintroduce Φeat, a novel material-grounded visual backbone that encour-ages a representation sensitive to material identity, including reflectanceand mesostructure. Instead of relying on generic data augmentations,we pretrain our model by contrasting observations of the same materialunder controlled variations in lighting and geometry. This encouragesinvariance to extrinsic factors while preserving sensitivity to intrinsicmaterial properties. We show that the resulting representation providesstrong priors for material-centric tasks, including feature-based mate-rial selection and classification. Our results demonstrate that physicallyinspired weak supervision is an effective strategy for learning represen-tations tailored to material perception.

ECCV 2026

OpenPanoD: Aligning Multimodal Prompts and Spherical Representations for Open-Vocabulary Panoramic Detection

Hang Xu

Panoramic object detection has made steady progress, butexisting methods remain largely closed-set and therefore cannot recog-nize novel categories in open environments. Extending open-vocabularydetection to panoramas is challenging because prompt-image alignmentis weakened by two factors: semantic ambiguity in language-only promptsand severe geometric distortion caused by equirectangular projection. Wepresent OpenPanoD, a framework for open-vocabulary panoramic de-tection that aligns multimodal prompts with geometry-aware sphericalrepresentations. On the prompt side, a multimodal prompt encoder com-bines language descriptions with visual exemplars to obtain category em-beddings that are both generalizable and visually specific. On the imageside, GeoFormer maps panoramic features onto a quasi-uniform spher-ical grid and performs spherical attention to reduce projection-induceddistortion and boundary discontinuity. The resulting prompt and imageembeddings are fused in a unified detection head for BFoV/RBFoV pre-diction. Experiments on 360-Indoor and PANDORA show that Open-PanoD consistently improves novel-category performance over strongopen-vocabulary and visual-prompt baselines. These results demonstratethe importance of jointly addressing semantic ambiguity and panoramicgeometry for open-vocabulary detection in 360-degree scenes.

ECCV 2026

WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation

Amogh Joshi, Julian Ost, Felix Heide

Unbounded 3D world generation is emerging as a founda-tional task for scene modeling in computer vision, graphics, and robotics.In this work, we present WorldFlow3D, a novel method capable of gen-erating unbounded 3D worlds. Building upon a foundational property offlow matching – namely, defining a path of transport between two datadistributions – we model 3D generation more generally as a problemof flowing through 3D data distributions, not limited to conditional de-noising. We find that our latent-free flow approach generates causal andaccurate 3D structure, and can use this as an intermediate distribution toguide the generation of more complex structure and high-quality texture– all while converging more rapidly than existing methods. We enablecontrollability over generated scenes with vectorized scene layout condi-tions for geometric structure control and visual texture control throughscene attributes. We confirm the effectiveness of WorldFlow3D on bothreal outdoor driving scenes and synthetic indoor scenes, validating cross-domain generalizability and high-quality generation on real data distri-butions. We confirm favorable scene generation fidelity over approachesin all tested settings for unbounded scene generation.

ECCV 2026

Vision-as-Inverse-Graphics Agent via Interleaved Multimodal Reasoning

Shaofeng Yin, Jiaxin Ge, Zora Wang, Xiuyu Li, Chenyang Wang, Michael Black, Trevor Darrell, Angjoo Kanazawa, Haiwen Feng

Vision-as-inverse-graphics, the concept of reconstructing im-ages into editable programs, remains challenging for Vision-LanguageModels (VLMs), which inherently lack fine-grained spatial grounding inone-shot settings. To address this, we introduce VIGA (Vision-as-Inverse-Graphics Agent), an interleaved multimodal reasoning framework wheresymbolic logic and visual perception actively cross-verify each other. VIGAoperates through a tightly coupled code-render-inspect loop: synthesizingsymbolic programs, projecting them into visual states, and inspectingdiscrepancies to guide iterative edits. Equipped with high-level semanticskills and an evolving multimodal memory, VIGA sustains evidence-basedmodifications over long horizons. This training-free, task-agnostic frame-work seamlessly supports 2D document generation, 3D reconstruction,multi-step 3D editing, and 4D physical interaction. Finally, we intro-duce BlenderBench, a challenging visual-to-code benchmark. Empirically,VIGA substantially improves accuracy compared with one-shot base-lines in BlenderGym (35.32% ), SlideBench (117.17% ) and our proposedBlenderBench (124.70% ).

ECCV 2026

HQ-DM: Single Hadamard Transformation-Based Quantization-Aware Training for Low-Bit Diffusion Models

Shizhuo Mao, Hongtao Zou, Qihu Xie, Song Chen, Yi Kang

Diffusion models have demonstrated significant applicationsin the field of image generation. However, their high computational andmemory costs pose challenges for deployment. Model quantization hasemerged as a promising solution to reduce storage overhead and acceler-ate inference. Nevertheless, existing quantization methods for diffusionmodels struggle to mitigate outliers in activation matrices during in-ference, leading to substantial performance degradation under low-bitquantization scenarios. To address this, we propose HQ-DM, a novelQuantization-Aware Training framework that applies Single HadamardTransformation to activation matrices. This approach effectively reducesactivation outliers while preserving model performance under quanti-zation. Compared to traditional Double Hadamard Transformation, ourproposed scheme offers distinct advantages by seamlessly supporting INTconvolution operations while preventing the amplification of weight out-liers. For conditional generation on the ImageNet 256×256 dataset usingthe LDM-4 model, our W4A4 and W4A3 quantization schemes improvethe Inception Score by 12.8% and 467.73%, respectively, over the existingstate-of-the-art method.

ECCV 2026

Vision Bridge Transformer at Scale

Zhenxiong Tan, Zeqing Wang, Xingyi Yang, Songhua Liu, Xinchao Wang

We introduce Vision Bridge Transformer (ViBT), alarge-scale instantiation of Brownian Bridge Models designed for con-ditional generation. Unlike traditional diffusion models that transformnoise into data, Bridge Models offer an efficient data-to-data translationparadigm by directly modeling the trajectory between inputs and out-puts. We first demonstrate the advantages of this paradigm through acontrolled apples-to-apples comparison against diffusion models, reveal-ing improved efficiency and performance in this setting. Building on this,we successfully scale the Bridge paradigm up to a 20B-parameter modelto tackle complex image and video translation tasks. To enable stabletraining at this scale, we adopt a Transformer architecture equipped witha variance-stabilized velocity matching objective. Together, these ad-vances unlock the potential of scaling Bridge Models, establishing strongperformance for instruction-based image editing and video stylization.

ECCV 2026

ViQ: Text-Aligned Visual Quantized Representations at Any Resolution

Xumin Yu, Zuyan Liu, Zhenyu Yang, Yuhao Dong, Shengsheng Qian, Jiwen Lu, Han Hu, Yongming Rao

A unified representation for text and vision is a natural pur-suit, as it enables simpler multimodal modeling and more efficient training.However, representing images as discrete signals in the same way as textinevitably introduces severe information loss. Existing work strugglesto balance low-level details and high-level semantics in discrete repre-sentations: reconstruction-oriented representations often lack semanticinformation, whereas semantically stronger features typically suffer fromsevere loss of detail. We present ViQ, a Visual Quantized Representationsframework, which is designed to balance semantics and details in discreterepresentations while supporting inputs at native resolutions, therebyenabling it to serve as a unified and general discrete representation forarbitrary visual inputs. Our approach structures quantization learninginto two stages: text-aligned pre-training and feature discretization. Withtext-aligned pre-training, we enhance the visual encoder semantic-richsupervision from the pretrained language model and enable it to pro-cess native-resolution visual inputs. During discretization, we proposea proximal representation learning strategy to progressively compactthe feature space, along with a position-aware head-wise quantizationmechanism that enables flexible processing of arbitrary resolutions. Ex-tensive experiments on multimodal tasks demonstrate that ViQ achievescompetitive performance compared to state-of-the-art multimodal visionencoders with continuous and high-dimensional visual features, whilemaintaining high precision in low-level reconstruction. We also showthat multimodal training with visual quantized representations largelyimproves efficiency, yielding up to 20%-70% acceleration with differentbase LLMs and training recipes.

ECCV 2026

Video-Holmes: Can MLLM Think like Holmes for Complex Video Reasoning?

Junhao Cheng, Yuying Ge, Teng Wang, Yixiao Ge, Jing Liao, Ying Shan

Recent advances in Chain-of-Thought reasoning have beenreported to enhance video reasoning capabilities of multimodal large lan-guage models (MLLMs). This progress naturally raises a question: Canthese models perform complex video reasoning in a manner comparable tohuman experts? However, existing video benchmarks primarily evaluatevisual perception and grounding, with questions that can be answeredbased on explicit prompts or isolated visual cues (e.g., “What is thewoman wearing?”). Such benchmarks do not fully capture the intricaciesof real-world reasoning, where humans must actively search for, integrate,and analyze multiple clues before reaching a conclusion. To address this,we introduce Video-Holmes, a benchmark inspired by the reasoningprocess of Sherlock Holmes, designed to evaluate the complex video rea-soning capabilities of MLLMs. Video-Holmes consists of 1,837 questionsderived from 270 manually annotated suspense short films, spanningseven carefully designed tasks. Each task is constructed by first identify-ing key events and causal relationships within films, and then designingquestions that require models to actively locate and connect multiplerelevant visual clues scattered across video clips. Evaluation of state-of-the-art MLLMs reveals that while these models generally excel at vi-sual perception, they encounter difficulties with integrating informationand often miss critical clues. For example, the best-performing model,Gemini-3.0-Pro, achieves an accuracy of only 49.6%, with most modelsscoring below 40%. We aim for Video-Holmes to serve as a “Holmes-test”for multimodal reasoning, motivating models to reason more like humansand emphasizing the ongoing challenges in this field.

ECCV 2026

Identity-Preserving Human Reconstruction from a Single Image via 3D Token Inference

Yanqi Bao, Jiaxiang Shang, Yang Gao, Yingchun Liu, Jing Huo, Jing Liao

We present the Identity-Preserving Large Human Recon-struction Model (IPRM), a feed-forward framework that reconstructsphotorealistic, clothed 3D humans from a single in-the-wild image whilepreserving 3D identity. Recent works predominantly infer 3D structurefrom 2D features, making it challenging to achieve 3D consistency andpreserve human identity in 3D space. To alleviate these challenges, IPRManchors the single-view 3D human reconstruction by constructing a human-based 3D feature space and explicitly preserves the human 3D identityfeatures during inference. Specifically, we introduce an efficient and ro-bust SMPL-based sparse voxel representation to transform 2D imageidentity features into 3D space, categorizing them into 3D visible iden-tity tokens and 3D invisible tokens to be inferred. Using these 3D tokens,an identity-aware 3D token inference module is proposed to propagateprojected 3D identity features from visible to invisible tokens, ensuringthat only unobserved regions are predicted while the observed identityremains intact. We further design an encoder–decoder architecture thatdecodes SMPL-based 3D features into either a 3D Gaussian Splatting ormesh representation, and augment it with a 3D ID Adapter for identitypreservation. Instead of conventional conditioning on image tokens (2DID Adapter), this adapter utilizes 3D identity tokens extracted from aparallel identity branch as guidance to inject token-wise identity infor-mation. Comprehensive experiments on existing benchmarks and in-the-wild data show IPRM surpasses state-of-the-art methods in reconstruc-tion performance, efficiency, 3D consistency and identity consistency.

ECCV 2026

VDC-Agent: When Video Detailed Captioners Evolve Themselves via Agentic Self-Reflection

Qiang Wang, Xinyuan Gao, SongLin Dong, Jizhou Han, Jiangyang Li, Yuhang He, Zhiheng Ma, Yihong Gong

Existing Video Detailed Captioning (VDC) methods pre-dominantly rely on costly human annotations or distillation from pow-erful proprietary models, creating a dependency on external supervision.In this paper, we propose VDC-Agent, an autonomous self-evolvingframework that empowers a single Multimodal Large Language Model(MLLM) to generate and refine high-quality captions through principle-guided self-reflection. To overcome the inference latency inherent in it-erative refinement, we further propose to internalize this reflective ca-pability into the model. Specifically, we construct VDC-Agent-19K, apreference dataset derived from the agent’s self-scored trajectories, andintroduce a Curriculum Direct Preference Optimization (DPO) strategy.This strategy leverages the quality gap between generated candidates toprogressively align the model from easy to hard samples. Extensive ex-periments demonstrate that VDC-Agent achieves state-of-the-art perfor-mance on VDC and DREAM-1K benchmarks, generating captions withsuperior detail and faithfulness. Crucially, our internalization strategyretains the inference efficiency of the base model while significantly en-hancing its generalization capabilities, as validated by both quantitativemetrics and human evaluation.

ECCV 2026

Towards Unsupervised Multi-modal Semantic Segmentation

Haitian Zhang, Thai Nguyen, Xiangyuan Wang, Mohan Liu, Addison Wang

Multi-modal semantic segmentation (MSS) is essential forrobust perception in complex environments, yet its potential remainslargely untapped due to the prohibitive cost of human annotations. Whileunsupervised semantic segmentation (USS) has seen success on single RGBmodality, its naive extension to multi-modal data is hamperedby fusion degradation. This is because, in the absence of explicitsupervision, existing frameworks struggle to reconcile the heterogeneousstructural patterns captured by different sensors, failing to effectivelyexploit their complementary information. In this paper, we make thefirst attempt to address the novel problem of Unsupervised Multi-modal Semantic Segmentation (UMSS), aiming to effectively exploitcomplementary sensor information in a fully label-free setting. To thisend, we propose UniM2 (Unified Multi-Modal), a novel frameworkbuilt upon DINOv3 that transforms conventional fusion methods intoconsistent performance gains. Our key idea is to learn a unified latentspace driven by Cross-modal Correspondence Synergy (CMCS) toextract intrinsic shared semantic cues, bypassing the need for label-guidedadaptive fusion. To mitigate inherent inter-modal conflicts, we introducea Cross-modal Harmonizer (CMH) that designates RGB as a stablereference, effectively suppressing inconsistent relational supervision whileguiding the model to exploit complementary structural features. Extensiveexperimental results on NYU-Depth-v2 and MFNet show that UniM2improves mIoU by 6.4% and 9.8%, respectively, demonstrating clearadvantages over existing frameworks in UMSS task.

ECCV 2026

FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography

Wei Qian, Dan Guo, Jinxing Zhou, Bochao Zou, Zitong Yu, Meng Wang

Remote photoplethysmography (rPPG) enables contactlessphysiological monitoring by capturing subtle skin-color variations fromfacial videos. However, most existing methods predominantly rely ontime-domain modeling, making them vulnerable to motion artifacts andillumination fluctuations, where weak physiological clues are easily over-whelmed by noise. To address these challenges, we propose FreqPhys,a frequency-guided rPPG framework that explicitly leverages physio-logical frequency priors for robust signal recovery. Specifically, Freq-Phys first applies a Physiological Bandpass Filtering module to sup-press out-of-band interference, and then performs Physiological Spec-trum Modulation together with adaptive spectral selection to empha-size pulse-related frequency components while suppress residual in-bandnoise. A Cross-domain Representation Learning module further fusesthese spectral priors with deep time-domain features to capture infor-mative spatial–temporal dependencies. Finally, a frequency-aware con-ditional diffusion process progressively reconstructs high-fidelity rPPGsignals. Extensive experiments on six benchmarks demonstrate that Fre-qPhys yields significant improvements over state-of-the-art approaches,particularly under challenging motion conditions. It highlights the im-portance of explicitly modeling physiological frequency priors. Our codeis available at the https://github.com/WeiQian98/FreqPhys.

ECCV 2026

TASE: Truncation-Aware Semantic Embeddings for 3D Scene Understanding and Editing

Tim-Felix Faasch, Jochen Kall, Jens Behley, Lucas Nunes, Cyrill Stachniss

High-fidelity semantic 3D scene representations are crucialfor numerous applications, including robotics, autonomous driving, andsimulation. Beyond this, the ability to edit such representations enablesdevelopers to adapt these applications more easily to specific target sce-narios. Current approaches provide limited support for controllable edit-ing. We introduce TASE, a method that projects pretrained 2D seman-tic features into a truncation-aware embedding space to enable flexi-ble 3D scene editing. Our method explicitly optimizes a feature spacein which progressively reducing feature channels yields increasingly ab-stract semantic representations, while retaining more channels preservesfine-grained detail. Additionally, we improve multi-view consistency ofthe features using a scale- and translation-consistency loss. The result-ing truncation-aware embedding space enables text-driven edits to 3Dscenes, providing explicit control over how strongly edits adhere to theoriginal scene content and allowing more substantial modifications thanprior methods. Moreover, we propose a finetuning stage for the edit-ing image generation model to mitigate artifacts caused by geometricchanges. Experimental results demonstrate competitive performance in3D scene editing, substantially outperforming prior methods on editsinvolving large geometric modifications.

ECCV 2026

SWSL: Semantic-aware Weakly Supervised Learning for 3D Motion Generation using 2D Motion Data

Zhicheng Shi, Tuo Feng, Wenguan Wang, Yi Yang

Data scarcity is a major constraint for data-driven Text-to-3D Motion generation. Prior attempts exploit abundant 2D motion datato address it, but typically treat 3D generation as a post-hoc stage of 2Dprediction, resulting in camera-pose-dependent multi-stage pipelines andunderutilization of rich 2D motion semantics (e.g., temporal dynamics).To respond, we propose SWSL, a Semantic-aware Weakly SupervisedLearning framework that leverages 2D-motion semantics to directly opti-mize end-to-end Text-to-3D Motion models. SWSL comprises three mod-ules: Semantic Space Construction (SSC), which first constructs a sharedsemantic space aligning text, 2D-motion, and 3D-motion; Semantic-awarePseudo-label Enhancement (SPE), which then uses weak supervisionfrom 2D-motion embeddings to refine 3D pseudo-labels; and Semantic-level Feedback Optimization (SFO), which finally supplies embedding-level feedback for optimization. SWSL offers: (i) reliance reduction ontext-3D motion data by utilizing text-2D motion pairs; (ii) 2D data con-straint looseness by extracting semantics from single-view, easy-accessible2D motions; and (iii) scalability along mainstream Text-to-3D Motionadvancement. On HumanML3D, SWSL yields consistent FID reductionsunder both a domain-specific protocol and a more general TMR-basedprotocol: -0.012 and -0.017 on simple baselines, along with -0.010 on acomplicated one in a domain-specific setting, and -0.048, -0.038, and -0.056 under TMR setting - demonstrating its efficacy, robustness, andgeneralization beyond the domain-specific bias.

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