GenAI4ELab/papercli-papers huggingface dataset:
Phased DMD: Few-step Distribution Matching Distillation via Score Matching within Subintervals
Authors: Xiangyu Fan; Zesong Qiu; Zhuguanyu Wu; Fanzhou Wang; Zhiqian Lin; Tianxiang Ren; Dahua Lin; Ruihao Gong; Lei Yang
Venue: CVPR 2026 (main)

Abstract: Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling trajectories of their teachers. Yet, the limited capacity of one-step distilled models compromises generative diversity and degrades performance in complex generative tasks, e.g.,  generating intricate object motions in text-to-video task. Directly extending DMD to multi-step distillation increases memory usage and computational depth, leading to instability and reduced efficiency.  While prior works propose stochastic gradient truncation as a potential solution, we observe that it substantially reduces the generative diversity in text-to-image generation and slows motion dynamics in video generation, reducing performance to the level of one-step models. To address these limitations, we propose Phased DMD, a multi-step distillation framework that bridges the idea of phase-wise distillation with Mixture-of-Experts (MoE), reducing learning difficulty while enhancing model capacity. Phased DMD incorporates two key ideas: progressive distribution matching and score matching within subintervals. First, our model divides the SNR range into subintervals, progressively refining the model to higher SNR levels, to better capture complex distributions. Next, to ensure accurate training within each subinterval, we derive rigorous mathematical formulations for the objective. We validate Phased DMD by distilling state-of-the-art image and video generation models, including Qwen-Image-20B and Wan2.2-28B. Experiments demonstrate that Phased DMD enhances motion dynamics, improves visual fidelity in video generation, and increases output diversity in image generation. Our code and models are available at https://x-niper.github.io/projects/Phased-DMD/.
EffectErase: Joint Video Object Removal and Insertion for High-Quality Effect Erasing
Authors: Yang Fu; Yike Zheng; Ziyun Dai; Henghui Ding
Venue: CVPR 2026 (main)

Abstract: Video object removal aims to eliminate dynamic target objects and their visual effects, such as deformation, shadows, and reflections, while restoring seamless backgrounds. Recent diffusion-based video inpainting and object removal methods can remove the objects but often struggle to erase these effects and to synthesize coherent backgrounds. Beyond method limitations, progress is further hampered by the lack of a comprehensive dataset that systematically captures common object effects across varied environments for training and evaluation. To address this, we introduce **VOR** (**V**ideo **O**bject **R**emoval), a large-scale dataset that provides diverse paired videos, each consisting of one video where the target object is present with its effects and a counterpart where the object and effects are absent, with corresponding object masks. VOR contains 60k high-quality video pairs from captured and synthetic sources, covers five effects types, and spans a wide range of object categories as well as complex, dynamic multi-object scenes. Building on VOR, we propose ***EffectErase***, an effect-aware video object removal method that treats video object insertion as the inverse auxiliary task within a reciprocal learning scheme. The model includes task-aware region guidance that focuses learning on affected areas and enables flexible task switching. Then, an insertion-removal consistency objective that encourages complementary behaviors and shared localization of effect regions and structural cues. Trained on VOR, EffectErase achieves superior performance in extensive experiments, delivering high-quality video object effect erasing across diverse scenarios.
SOUPLE: Enhancing Audio-Visual Localization and Segmentation with Learnable Prompt Contexts
Authors: Khanh Binh Nguyen; Chae Jung Park
Venue: CVPR 2026 (main)

Abstract: Large-scale pre-trained image-text models exhibit robust multimodal representation, yet applying contrastive language-image pretraining (CLIP) to audio-visual localization remains challenging. Replacing the classification token ([CLS]) with an audio-embedded token ([V_A])struggles to capture semantic cues, and the prompt "a photo of a [V_A]" fails to establish meaningful connections between audio embeddings and context tokens. To address these issues, we propose sound-aware prompt learning (SouPLe), which replaces fixed prompts with learnable context tokens. These tokens incorporate visual features to generate conditional context for a mask decoder, effectively bridging semantic correspondence between audio and visual inputs. Experiments on VGGSound, SoundNet, and AVSBench confirm that SouPLe significantly improves localization and segmentation performance.
ReDirector: Creating Any-Length Video Retakes with Rotary Camera Encoding
Authors: Byeongjun Park; Byung-Hoon Kim; Hyungjin Chung; Jong Chul Ye
Venue: CVPR 2026 (main)

Abstract: We present ReDirector, a novel camera-controlled video retake generation method for dynamically captured variable-length videos. In particular, we rectify a common misuse of RoPE in previous works by aligning the spatiotemporal positions of the input video and the target retake. Moreover, we introduce Rotary Camera Encoding (RoCE), a camera-conditioned RoPE phase shift that captures and integrates multi-view relationships within and across the input and target videos. By integrating camera conditions into RoPE, our method generalizes to out-of-distribution camera trajectories and video lengths, yielding improved dynamic object localization and static background preservation. Extensive experiments further demonstrate significant improvements in camera controllability, geometric consistency, and video quality across various trajectories and lengths.
Heuristic-inspired Reasoning Priors Facilitate Data-Efficient Referring Object Detection
Authors: Xu Zhang; Zhe Chen; Jing Zhang; Dacheng Tao
Venue: CVPR 2026 (main)

Abstract: Most referring object detection (ROD) models, especially the modern grounding detectors, are designed for data-rich conditions, yet many practical deployments, such as robotics, augmented reality, and other specialized domains, would face severe label scarcity. In such regimes, end-to-end grounding detectors need to learn spatial and semantic structure from scratch, wasting precious samples. We ask a simple question: Can explicit reasoning priors help models learn more efficiently when data is scarce? To explore this, we first introduce a Data-efficient Referring Object Detection (De-ROD) task, which is a benchmark protocol for measuring ROD performance in low-data and few-shot settings. We then propose the HeROD (Heuristic-inspired ROD), a lightweight, model-agnostic framework that injects explicit, heuristic-inspired spatial and semantic reasoning priors, which are interpretable signals derived based on the referring phrase, into 3 stages of a modern DETR-style pipeline: proposal ranking, prediction fusion, and Hungarian matching. By biasing both training and inference toward plausible candidates, these priors promise to improve label efficiency and convergence performance. On RefCOCO, RefCOCO+, and RefCOCOg, HeROD consistently outperforms strong grounding baselines in scarce-label regimes. More broadly, our results suggest that integrating simple, interpretable reasoning priors provides a practical and extensible path toward better data-efficient vision-language understanding. Code at: https://github.com/xuzhang1199/HeROD.
GaussianFluent: Gaussian Simulation for Dynamic Scenes with Mixed Materials
Authors: Bei Huang; Yixin Chen; Ruijie Lu; Gang Zeng; Hongbin Zha; Yuru Pei; Siyuan Huang
Venue: CVPR 2026 (main)

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a prominent 3D representation for high-fidelity and real-time rendering. Prior work has coupled physics simulation with Gaussians, but predominantly targets soft, deformable materials, leaving brittle fracture largely unresolved. This stems from two key obstacles: the lack of volumetric interiors with coherent textures in GS representation, and the absence of fracture-aware simulation methods for Gaussians. To address these challenges, we introduce GaussianFluent, a unified framework for realistic simulation and rendering of dynamic object states. First, it synthesizes photorealistic interiors by densifying internal Gaussians guided by generative models. Second, it integrates an optimized Continuum Damage Material Point Method (CD-MPM) to enable brittle fracture simulation at remarkably high speed. Our approach handles complex scenarios including mixed-material objects and multi-stage fracture propagation, achieving results infeasible with previous methods. Experiments clearly demonstrate GaussianFluent's capability for photo-realistic, real-time rendering with structurally consistent interiors, highlighting its potential for downstream application, such as VR and Robotics.
SoC: Semantic Orthogonal Calibration for Test-Time Prompt Tuning
Authors: Leo Fillioux; Omprakash Chakraborty; Ismail Ben Ayed; Paul-Henry Cournède; Stergios Christodoulidis; Maria Vakalopoulou; Jose Dolz
Venue: CVPR 2026 (main)

Abstract: With the increasing adoption of vision-language models (VLMs) in critical decision-making systems such as in healthcare or autonomous driving, the calibration of their uncertainty estimates has become paramount. Yet, this dimension has been largely underexplored in the VLM test-time prompt-tuning (TPT) literature, which has prioritized on improving their discriminative performance. Recent state-of-the-art methods advocate for enforcing full orthogonality over pairs of text prompt embeddings to enhance separability, and therefore calibration. Nevertheless, as we theoretically show in this work, the inherent gradients from fully orthogonal constraints will strongly push semantically related classes away, ultimately making the model overconfident. Based on our findings, we propose Semantic Orthogonal Calibration (SoC), a Huber-based regularizer that enforces smooth prototype separation while preserving semantic proximity, thereby improving calibration compared to prior orthogonality-based approaches. Across a comprehensive empirical validation, we demonstrate that SoC consistently improves calibration performance, while also maintaining competitive discriminative capabilities. Our code is available at github.com/leofillioux/SoC.
Learning a Unified Latent Action Space from Videos with Action-centric Cycle Consistency
Authors: Guangyan Chen; Qi Shao; Te Cui; Zichen Zhou; Weixin Mao; Luojie Yang; Meiling Wang; Yi Yang; Hua Chen; Yufeng Yue
Venue: CVPR 2026 (main)

Abstract: Video data provides a rich source beyond expensive action-labeled data for advancing robot learning. Recent approaches have demonstrated promising potential in leveraging video data by learning latent actions for policy training. The latent action tokenizer encodes latent actions between successive video frames, and the tokenizer is trained to reconstruct future frames using current frames and the encoded latent actions. However, the unique pairing of successive frames permits future frame reconstruction with little understanding of transition dynamics, hindering the learning of semantically consistent latent actions. Moreover, the tokenizer typically allocates distinct latent action subsets to individual embodiments to accommodate heterogeneous morphologies, constraining knowledge transfer. To overcome such limitations, we propose the action-centric cycle consistency, aiming to establish a unified latent action space. Our method samples latent actions from the latent action space and decodes them with video frames to generate diverse subsequent frames, then enforces cycle consistency by predicting the sampled actions from both original and generated frames. Our concise method creates a challenging task that learns corresponding latent actions from current frames and diverse generated future frames, compelling the tokenizer to develop semantically consistent action representations. Additionally, sampled latent actions can be applied to video frames from distinct embodiments, facilitating the alignment of latent actions across embodiments. Experiments demonstrate that our approach achieves a 20.1% improvement over OpenVLA on the LIBERO benchmark and increases the average length from 3.27 to 3.93 on the CALVIN benchmark. In real-world experiments, our method maintains strong performance with a 44% improvement.
FaceCam: Portrait Video Camera Control via Scale-Aware Conditioning
Authors: Weijie Lyu; Ming-Hsuan Yang; Zhixin Shu
Venue: CVPR 2026 (main)

Abstract: We introduce FaceCam, a system that generates video under customizable camera trajectories for monocular human portrait video input. Recent camera control approaches based on large video-generation models have shown promising progress but often exhibit geometric distortions and visual artifacts on portrait videos due to scale-ambiguous camera representations or 3D reconstruction errors. To overcome these limitations, we propose a face-tailored scale-aware representation for camera transformations that provides deterministic conditioning without relying on 3D priors. We train a video generation model on both multi-view studio captures and in-the-wild monocular videos, and introduce two camera-control data generation strategies: synthetic camera motion and multi-shot stitching, to exploit stationary training cameras while generalizing to dynamic, continuous camera trajectories at inference time. Experiments on Ava-256 dataset and diverse in-the-wild videos demonstrate that FaceCam achieves superior performance in camera controllability, visual quality, identity and motion preservation.
Language-Free Generative Editing from One Visual Example
Authors: Omar Elezabi; Eduard Zamfir; Zongwei Wu; Radu Timofte
Venue: CVPR 2026 (main)

Abstract: Text-guided diffusion models have advanced image editing by enabling intuitive control through language. However, despite their strong capabilities, we surprisingly find that SOTA methods struggle with simple, everyday transformations such as rain or blur. We attribute this limitation to weak and inconsistent textual supervision during training, which leads to poor alignment between language and vision. Existing solutions often rely on extra finetuning or stronger text conditioning, but suffer from high data and computational requirements. We argue that diffusion-based editing capabilities aren't lost but merely hidden from text. The door to cost-efficient visual editing remains open, and the key lies in a vision-centric paradigm that perceives and reasons about visual change as humans do, beyond words. Inspired by this, we introduce Visual Diffusion Conditioning (VDC), a training-free framework that learns conditioning signals directly from visual examples for precise, language-free image editing. Given a paired example--one image with and one without the target effect--VDC derives a visual condition that captures the transformation and steers generation through a novel condition-steering mechanism. An accompanying inversion-correction step mitigates reconstruction errors during DDIM inversion, preserving fine detail and realism. Across diverse tasks, VDC outperforms both training-free and fully fine-tuned text-based editing methods. The code and models are open-sourced at https://omaralezaby.github.io/vdc/
ORION: ORthonormal Text Encoding for Universal VLM AdaptatION
Authors: Omprakash Chakraborty; Jose Dolz; Ismail Ben Ayed
Venue: CVPR 2026 (main)

Abstract: Vision-language models (VLMs) have demonstrated remarkable generalization across diverse tasks, yet their performance remains constrained by the quality and geometry of the textual prototypes used to represent classes. Standard zero-shot classifiers, derived from frozen text encoders and handcrafted prompts, may yield correlated or weakly separated embeddings that limit task-specific discriminability. We introduce ORION, a text encoder fine-tuning framework that improves pretrained VLMs using only class names. Our method optimizes, via low-rank adaptation, a novel loss integrating two terms, one promoting pairwise orthogonality between the textual representations of the classes of a given task and the other penalizing deviations from the initial class prototypes. Furthermore, we provide a probabilistic interpretation of our orthogonality penalty, connecting it to the general maximum likelihood estimation (MLE) principle via Huygens' theorem. We report extensive experiments on 11 benchmarks and three large VLM backbones, showing that the refined textual embeddings yield powerful replacements for the standard CLIP prototypes. Added as plug-and-play module on top of various state-of-the-art methods, and across different prediction settings (zero-shot, few-shot and test-time adaptation), ORION improves the performance consistently and significantly.
Mobile-VTON: High-Fidelity On-Device Virtual Try-On
Authors: Zhenchen Wan; Ce Chen; Runqi Lin; Jiaxin Huang; Tianxi Chen; Yanwu Xu; Tongliang Liu; Mingming Gong
Venue: CVPR 2026 (main)

Abstract: Virtual try-on (VTON) has recently achieved impressive visual fidelity, but most existing systems require uploading personal photos to cloud-based GPUs, raising privacy concerns and limiting on-device deployment. To address this, we present Mobile-VTON, a high-quality, privacy-preserving framework that enables fully offline virtual try-on on commodity mobile devices using only a single user image and a garment image. Mobile-VTON introduces a modular TeacherNet--GarmentNet--TryonNet (TGT) architecture that integrates knowledge distillation, garment-conditioned generation, and garment alignment into a unified pipeline optimized for on-device efficiency. Within this framework, we propose a Feature-Guided Adversarial (FGA) Distillation strategy that combines teacher supervision with adversarial learning to better match real-world image distributions. GarmentNet is trained with a trajectory-consistency loss to preserve garment semantics across diffusion steps, while TryonNet uses latent concatenation and lightweight cross-modal conditioning to enable robust garment-to-person alignment without large-scale pretraining. By combining these components, Mobile-VTON achieves high-fidelity generation with low computational overhead. Experiments on VITON-HD and DressCode at 1024 x 768 show that it matches or outperforms strong server-based baselines while running entirely offline. These results demonstrate that high-quality VTON is not only feasible but also practical on-device, offering a secure solution for real-world applications.
EmoTaG: Emotion-Aware Talking Head Synthesis on Gaussian Splatting with Few-Shot Personalization
Authors: Haolan Xu; Keli Cheng; Lei Wang; Ning Bi; Xiaoming Liu
Venue: CVPR 2026 (main)

Abstract: Audio-driven 3D talking head synthesis has advanced rapidly with Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). By leveraging rich pre-trained priors, few-shot methods enable instant personalization from just a few seconds of video. However, under expressive facial motion, existing few-shot approaches often suffer from geometric instability and audio-emotion mismatch, highlighting the need for more effective emotion-aware motion modeling. In this work, we present EmoTaG, a few-shot emotion-aware 3D talking head synthesis framework built on the Pretrain-and-Adapt paradigm. Our key insight is to reformulate motion prediction in a structured FLAME parameter space rather than directly deforming 3D Gaussians, thereby introducing explicit geometric priors that improve motion stability. Building upon this, we propose a Gated Residual Motion Network (GRMN), which captures emotional prosody from audio while supplementing head pose and upper-face cues absent from audio, enabling expressive and coherent motion generation. Extensive experiments demonstrate that EmoTaG achieves state-of-the-art performance in emotional expressiveness, lip synchronization, visual realism, and motion stability.
RINO: Rotation-Invariant Non-Rigid Correspondences
Authors: Maolin Gao; Shao Jie Hu-Chen; Congyue Deng; Riccardo Marin; Leonidas Guibas; Daniel Cremers
Venue: CVPR 2026 (main)

Abstract: Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcrafted descriptors, limiting their effectiveness under non-isometric deformations, partial data, and non-manifold inputs. To overcome these issues, we introduce RINO, an unsupervised, rotation-invariant dense correspondence framework that effectively unifies rigid and non-rigid shape matching. The core of our method is the novel RINONet, a feature extractor that integrates vector-based SO(3)-invariant learning with orientation-aware complex functional maps to extract robust features directly from raw geometry. This allows for a fully end-to-end, data-driven approach that bypasses the need for shape pre-alignment or handcrafted features. Extensive experiments show unprecedented performance of RINO across challenging non-rigid matching tasks, including arbitrary poses, non-isometry, partiality, non-manifoldness, and noise.
Native and Compact Structured Latents for 3D Generation
Authors: Jianfeng Xiang; Xiaoxue Chen; Sicheng Xu; Ruicheng Wang; Zelong Lv; Yu Deng; Hongyuan Zhu; Yue Dong; Hao Zhao; Nicholas Jing Yuan; Jiaolong Yang
Venue: CVPR 2026 (main)

Abstract: Recent advancements in 3D generative modeling have significantly improved the generation realism, yet the field is still hampered by existing representations, which struggle to capture assets with complex topologies and detailed appearance. This paper present an approach for learning a structured latent representation from native 3D data to address this challenge. At its core is a new sparse voxel structure called O-Voxel, an omni-voxel representation that encodes both geometry and appearance. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, while capturing comprehensive surface attributes beyond texture color, such as physically-based rendering parameters. Based on O-Voxel, we design a Sparse Compression VAE which provides a high spatial compression rate and a compact latent space. We train large-scale flow-matching models comprising 4B parameters for 3D generation using diverse public 3D asset datasets. Despite their scale, inference remains highly efficient. Meanwhile, the geometry and material quality of our generated assets far exceed those of existing models. We believe our approach offers a significant advancement in 3D generative modeling.