GenAI4ELab/papercli-papers huggingface dataset:
SimScale: Learning to Drive via Real-World Simulation at Scale
Authors: Haochen Tian; Tianyu Li; Haochen Liu; Jiazhi Yang; Yihang Qiu; Guang Li; Junli Wang; Yinfeng Gao; Zhang Zhang; Liang Wang; Hangjun Ye; Long Chen; Hongyang Li
Venue: CVPR 2026 (main)

Abstract: Achieving fully autonomous driving systems requires learning rational decisions in a wide span of scenarios, including safety-critical and out-of-distribution ones. However, such cases are underrepresented in real-world corpus collected by human experts. To complement for the lack of data diversity, we introduce a novel and scalable simulation framework capable of synthesizing massive unseen states upon existing driving logs. Our pipeline utilizes advanced neural rendering with a reactive environment to generate high-fidelity multi-view observations controlled by the perturbed ego trajectory. Furthermore, we develop a pseudo-expert trajectory generation mechanism for these newly simulated states to provide action supervision. Upon the synthesized data, we find that a simple co-training strategy on both real-world and simulated samples can lead to significant improvements in both robustness and generalization for various planning methods on challenging real-world benchmarks, up to +8.6 EPDMS on navhard and +2.9 on navtest. More importantly, such policy improvement scales smoothly by increasing simulation data only, even without extra real-world data streaming in. We further reveal several crucial findings of such a sim-real learning system, which we term SimScale, including the design of pseudo-experts and the scaling properties for different policy architectures. Simulation data and code have been released at https://github.com/OpenDriveLab/SimScale.
StyleTextGen: Style-Conditioned Multilingual Scene Text Generation
Authors: Zeyu Chen; Fangmin Zhao; Yan Shu; Yichao Liu; Liu Yu; Yu Zhou
Venue: CVPR 2026 (main)

Abstract: Style-conditioned scene text generation faces unique challenges in extracting precise text styles from complex backgrounds and maintaining fine-grained style consistency across characters, especially for multilingual scripts. We propose StyleTextGen, a novel framework that learns to perceive and replicate visual text styles across different languages and writing systems. Our approach features three key contributions: First, we introduce a dual-branch style encoder dedicated to style modeling, yielding robust multilingual text style representations in complex real-world scenes. Second, we design a text style consistency loss that enhances style coherence and improves overall visual quality. Third, we develop a mask-guided inference strategy that ensures precise style alignment between generated and reference text. To facilitate systematic evaluation, we construct StyleText-CE, a bilingual scene text style benchmark covering both monolingual and cross-lingual settings. Extensive experiments demonstrate that StyleTextGen significantly outperforms existing methods in style consistency and cross-lingual generalization, establishing new state-of-the-art performance in multilingual style-conditioned text generation.
Single-step Diffusion-based Video Coding with Semantic-Temporal Guidance
Authors: Naifu Xue; Zhaoyang Jia; Jiahao Li; Bin Li; Zihan Zheng; Yuan Zhang; Yan Lu
Venue: CVPR 2026 (main)

Abstract: While traditional and neural video codecs (NVCs) have achieved remarkable rate-distortion performance, improving perceptual quality at low bitrates remains challenging. Some NVCs incorporate perceptual or adversarial objectives but still suffer from artifacts due to limited generation capacity, whereas others leverage pretrained diffusion models to improve quality at the cost of high sampling complexity. To overcome these challenges, we propose S2VC, a Single Step diffusion-based Video Codec that integrates a conditional coding framework with an efficient single-step diffusion generator, enabling realistic reconstruction at low bitrates with reduced sampling cost. Recognizing the importance of semantic conditioning in single-step diffusion, we introduce Contextual Semantic Guidance to extract frame-adaptive semantics from buffered features. This guidance replaces text captions with efficient, fine-grained conditioning, thereby improving generation realism. In addition, Temporal Consistency Guidance is incorporated into the diffusion U-Net to enforce temporal coherence across frames and ensure stable generation. Extensive experiments show that S2VC delivers state-of-the-art perceptual quality with an average bitrate saving of 51.62% over prior perceptual method, underscoring the promise of single-step diffusion for efficient, high-quality video compression. Project: https://onedc-codec.github.io/s2vc/
Unified Customized Generation by Disentangled Reward Modeling
Authors: Shaojin Wu; Mengqi Huang; Yufeng Cheng; Wenxu Wu; Jiahe Tian; Yiming Luo; Fei Ding; Qian He
Venue: CVPR 2026 (main)

Abstract: Existing literature typically treats various customized generation tasks (e.g., subject-customized generation, style-customized generation) as distinct and disjoint problems, with each task focusing solely on customizing a specific aspect of the reference image. However, we argue that the objectives of these different customization tasks are inherently complementary and can be mutually enhanced within a unified framework, as they fundamentally involve the disentanglement of multiple feature aspects from the reference image. To this end, we introduce USO, a Unified Simultaneous Optimization framework to simultaneously unify different customized tasks (i.e., subject and style). Specifically, USO introduces a cyclical data-model framework that connects these two tasks by a subject-for-style data curation pipeline and a style-for-subject model training pipeline. The subject-for-style data curation pipeline leverages a state-of-the-art subject-customized model to generate high-quality triplet data comprising content images, style images, and their corresponding stylized content images. Building on this foundation, the style-for-subject model training pipeline introduces an auxiliary style reward to simultaneously align style and content features, thereby reinforcing the model's ability to extract the desired style or content features from the reference image. Extensive experiments demonstrate that USO achieves state-of-the-art performance among open-source models, excelling in both subject consistency and style similarity. Code and model: https://github.com/bytedance/USO.
AeroGS: Scale-Aware Gaussian Splatting for Pose-Free Dynamic UAV Scene Reconstruction
Authors: Tingyun Li; Xinyi Liu; Yongjun Zhang; Yi Wan; Xiaoan Liu; Weiwei Fan; Jiahao Liu
Venue: CVPR 2026 (main)

Abstract: Monocular UAV videos pose a fundamental challenge for 3D reconstruction: dynamic scene modeling requires accurate camera poses, yet recovering poses from long UAV trajectories often fails in texture-sparse regions and in the presence of moving objects. Existing approaches typically handle either pose-free static reconstruction or dynamic reconstruction with known poses, but jointly solving both from casual aerial footage remains difficult due to motion coupling and severe scale variation. We introduce AeroGS, a scale-aware Gaussian splatting framework that jointly recovers camera trajectories and reconstructs dynamic scenes from pose-free monocular videos. Central to our method are scale-aware spatio-temporal anchors (S2A-Anchors), which enable a unified optimization via three key decoupling mechanisms: (i) separating ego-motion from object motion, (ii) isolating static geometry from temporal deformation, and (iii) adapting to scale variation between distant terrain and nearby objects. This design effectively stabilizes optimization under large motion and scale imbalance. Extensive experiments on UAV and driving benchmarks show that AeroGS achieves state-of-the-art rendering quality (PSNR/LPIPS), precise trajectory recovery (ATE/RPE), and faithful motion reconstruction, consistently surpassing recent pose-free baselines.
Neural Collapse in Test-Time Adaptation
Authors: Xiao Chen; Zhongjing Du; Jiazhen Huang; Xu Jiang; Li Lu; Jingyan Jiang; Zhi Wang
Venue: CVPR 2026 (main)

Abstract: Test-Time Adaptation (TTA) enhances model robustness to out-of-distribution (OOD) data by updating the model online during inference, yet existing methods lack theoretical insights into the fundamental causes of performance degradation under domain shifts. Recently, Neural Collapse (NC) has been proposed as an emergent geometric property of deep neural networks (DNNs), providing valuable insights for TTA. In this work, we extend NC to the sample-wise level and discover a novel phenomenon termed Sample-wise Alignment Collapse (NC3+), demonstrating that a sample's feature embedding, obtained by a trained model, aligns closely with the corresponding classifier weight. Building on NC3+, we identify that the performance degradation stems from sample-wise misalignment in adaptation which exacerbates under larger distribution shifts. This indicates the necessity of realigning the feature embeddings with their corresponding classifier weights. However, the misalignment makes pseudo-labels unreliable under domain shifts. To address this challenge, we propose NCTTA, a novel feature-classifier alignment method with hybrid targets to mitigate the impact of unreliable pseudo-labels, which blends geometric proximity with predictive confidence. Extensive experiments demonstrate the effectiveness of NCTTA in enhancing robustness to domain shifts. For example, NCTTA outperforms Tent by 14.52% on ImageNet-C. Project page is publicly available at https://github.com/Cevaaa/NCTTA.
Learning to Control Physically-simulated 3D Characters via Generating and Mimicking 2D Motions
Authors: Jianan Li; Xiao Chen; Tao Huang; Tien-Tsin Wong
Venue: CVPR 2026 (main)

Abstract: Video data is more cost-effective than motion capture data for learning 3D character controllers, yet using it to generate realistic and physically plausible motions remains challenging. Previous approaches typically rely on off-the-shelf motion reconstruction techniques to extract 3D kinematic trajectories for physics-based imitation. However, these approaches are fundamentally limited by the physical implausibility of the estimated motions and lack a unified framework for handling non-human subjects and complex human-object interactions (HOI). We tackle this challenge by introducing Mimic2DM, a novel motion imitation framework that learns the control policy directly and solely from 2D motions extracted from videos. By formulating a 2D motion tracking objective, we train a general single-view tracking policy capable of imitating arbitrary reference motions in a physics simulation using exclusively 2D data. Furthermore, by training on 2D motions from diverse viewpoints, our view-agnostic policy implicitly learns 3D motion structures, allowing it to perform full 3D motion tracking simply by aggregating multiple views. To extend this capability, we integrate the tracking policy with an autoregressive transformer-based 2D motion generator, constructing a hierarchical control framework capable of long-horizon motion synthesis and interactive control. We show that the proposed approach is versatile and effectively synthesizes physically plausible and diverse motions across a range of domains, including dancing, soccer dribbling, and animal locomotion, without any reliance on explicit 3D motion data.
DuoMo: Dual Motion Diffusion for World-Space Human Reconstruction
Authors: Yufu Wang; Evonne Ng; Soyong Shin; Rawal Khirodkar; Yuan Dong; Zhaoen Su; Jinhyung Park; Kris Kitani; Alexander Richard; Fabian Prada; Michael Zollhöfer
Venue: CVPR 2026 (main)

Abstract: We present DuoMo, a generative method that recovers human motion in world-space coordinates from unconstrained videos with noisy or incomplete observations. Reconstructing such motion requires solving a fundamental trade-off: generalizing from diverse and noisy video inputs while maintaining global motion consistency. Our approach addresses this problem by factorizing motion learning into two diffusion models. The camera-space model first estimates motion from videos in camera coordinates. The world-space model then lifts this initial estimate into world coordinates and refines it to be globally consistent. Together, the two models can reconstruct motion across diverse scenes and trajectories, even from highly noisy or incomplete observations. Moreover, our formulation is general, generating the motion of mesh vertices directly (bypassing parametric models). DuoMo achieves state-of-the-art performance. On EMDB, our method obtains a 16% reduction in world-space MPJPE error while maintaining low foot skating. On RICH, it obtains a 30% reduction in world-space MPJPE error. Project page: yufu-wang.github.io/duomo/
Towards Decompositional Human Motion Generation with Energy-Based Diffusion Models
Authors: Jianrong Zhang; Hehe Fan; Yi Yang
Venue: CVPR 2026 (main)

Abstract: Human motions are compositional: complex behaviors can be described as combinations of simpler primitives. However, existing approaches primarily focus on forward modeling, e.g., learning holistic mappings from text to motion or composing a complex motion from a set of motion concepts. In this paper, we consider the inverse perspective: decomposing a holistic motion into semantically meaningful sub-components. We propose DeMoGen, a compositional training paradigm for decompositional learning that employs an energy-based diffusion model. This energy formulation directly captures the composed distribution of multiple motion concepts, enabling the model to discover them without relying on ground-truth motions for individual concepts. Within this paradigm, we introduce three training variants to encourage a decompositional understanding of motion: 1. DeMoGen-Exp explicitly trains on decomposed text prompts; 2. DeMoGen-OSS performs orthogonal self-supervised decomposition; 3. DeMoGen-SC enforces semantic consistency between original and decomposed text embeddings. These variants enable our approach to disentangle reusable motion primitives from complex motion sequences. We also demonstrate that the decomposed motion concepts can be flexibly recombined to generate diverse and novel motions, generalizing beyond the training distribution. Additionally, we construct a text-decomposed dataset to support compositional training, serving as an extended resource to facilitate text-to-motion generation and motion composition.
HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generation
Authors: Junhao Hou; Chenqi Luo; Pufan Wang; Jiaying Lu; Yusheng Liu; Feiwei Qin; Meie Fang; Kun Zhou
Venue: CVPR 2026 (main)

Abstract: Boundary representation (B-Rep) generation is a fundamental task in computer-aided design, yet the direct synthesis of high-fidelity and structurally valid B-Reps remains a major challenge. Existing deep generative methods suffer from two forms of brittleness: representation brittleness, caused by padding noise and feature contamination in the latent space, and generation brittleness, stemming from sequential error propagation and a train-inference mismatch due to non-differentiable validity enforcement. We propose HiFi-BRep, a novel framework that addresses these limitations through two synergistic contributions. First, a topology-aware encoder constructs a high-fidelity latent representation by eliminating padding via learnable queries and preventing feature contamination with topology-guided attention. Second, a single-stage decoder jointly predicts geometry and topology in parallel, embedding core manifold constraints as a differentiable learning objective. This design ensures mutual guidance between geometry and topology while avoiding cascaded errors. Extensive experiments show that HiFi-BRep significantly outperforms state-of-the-art methods in both structural validity and geometric fidelity, providing a robust solution for high-quality B-Rep synthesis. Code and models are publicly available at https://github.com/1nnoh/HiFi-BRep.
ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation
Authors: Ayush Roy; Wei-Yang Alex Lee; Rudrasis Chakraborty; Vishnu Suresh Lokhande
Venue: CVPR 2026 (main)

Abstract: In recent times, large datasets hinder efficient model training while also containing redundant concepts. Dataset distillation aims to synthesize compact datasets that preserve the knowledge of large-scale training sets while drastically reducing storage and computation. Recent advances in diffusion models have enabled training-free distillation by leveraging pre-trained generative priors; however, existing guidance strategies remain limited. Current score-based methods either perform unguided denoising or rely on simple mode-based guidance toward instance prototype centroids (IPC centroids), which often are rudimentary and suboptimal. We propose Manifold-Guided Distillation (ManifoldGD), a training-free diffusion-based framework that integrates manifold consistent guidance at every denoising timestep. Our method employs IPCs computed via a hierarchical, divisive clustering of VAE latent features--yielding a multi-scale coreset of IPCs that captures both coarse semantic modes and fine intra-class variability. Using a local neighborhood of the extracted IPC centroids, we create the latent manifold for each diffusion denoising timestep. At each denoising step, we project the mode-alignment vector onto the local tangent space of the estimated latent manifold, thus constraining the generation trajectory to remain manifold-faithful while preserving semantic consistency. This formulation improves representativeness, diversity, and image fidelity without requiring any model retraining. Empirical results demonstrate consistent gains over existing training-free and training-based baselines in terms of FID, l_2 distance among real and synthetic dataset embeddings, and classification accuracy, establishing ManifoldGD as the first geometry-aware training-free data distillation framework.
Flash-DMD: Towards High-Fidelity Few-Step Image Generation with Efficient Distillation and Joint Reinforcement Learning
Authors: Guanjie Chen; Shirui Huang; Yifu Sun; Kai Liu; Jianchen Zhu; Xiaoye Qu; Yu Cheng; Peng Chen
Venue: CVPR 2026 (main)

Abstract: Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising technique to accelerate generation, but it often requires extensive training and leads to image quality degradation. Furthermore, fine-tuning these distilled models for specific objectives, such as aesthetic appeal or user preference, using Reinforcement Learning (RL) is notoriously unstable and easily falls into reward hacking. In this work, we introduce Flash-DMD, a novel framework that enables fast convergence with distillation and joint RL-based refinement.Specifically, we first propose an efficient timestep-aware distillation strategy that significantly reduces training cost with enhanced realism, outperforming DMD2 with only 2.1% its training cost. Second, we introduce a joint training scheme where the model is fine-tuned with an RL objective while the timestep distillation training continues simultaneously. We demonstrate that the stable, well-defined loss from the ongoing distillation acts as a powerful regularizer, effectively stabilizing the RL training process and preventing policy collapse. Extensive experiments on score-based and flow matching models show that our proposed Flash-DMD not only converges significantly faster but also achieves state-of-the-art generation quality in the few-step sampling regime, outperforming existing methods in visual quality, human preference, and text-image alignment metrics. Our work presents an effective paradigm for training efficient, high-fidelity, and stable generative models. Codes are attached in the supplementary.
TAP: A Token-Adaptive Predictor Framework for Training-Free Diffusion Acceleration
Authors: Haowei Zhu; Tingxuan Huang; Xing Wang; Tianyu Zhao; Jiexi Wang; Weifeng Chen; Xurui Peng; Fangmin Chen; Junhai Yong; Bin Wang
Venue: CVPR 2026 (main)

Abstract: Diffusion models achieve strong generative performance but remain slow at inference due to the need for repeated full-model denoising passes. We present Token-Adaptive Predictor (TAP), a training-free, probe-driven framework that adaptively selects a predictor for each token at every sampling step. TAP uses a single full evaluation of the model's first layer as a low-cost probe to compute proxy losses for a compact family of candidate predictors (instantiated primarily with Taylor expansions of varying order and horizon), then assigns each token the predictor with the smallest proxy error. This per-token "probe-then-select" strategy exploits heterogeneous temporal dynamics, requires no additional training, and is compatible with various predictor designs. TAP incurs negligible overhead while enabling large speedups with little or no perceptual quality loss. Extensive experiments across multiple diffusion architectures and generation tasks show that TAP substantially improves the accuracy-efficiency frontier compared to fixed global predictors and caching-only baselines.
Debiased Sample Selection for Learning with Noisy Labels
Authors: Weiran Pan; Wei Wei; Wenfeng Xie
Venue: CVPR 2026 (main)

Abstract: Existing methods for learning with noisy labels (LNL) predominantly rely on the small-loss trick, assuming that low-loss samples are more likely to be correctly labeled. While effective, this strategy suffers from two overlooked confirmation biases: (1) Class-level confirmation bias: samples from easy-to-learn classes tend to have lower losses, leading to over-selection of easy samples while ignore hard ones; (2) Instance-level confirmation bias: mislabeled samples with spuriously low loss are mistakenly treated as clean, forcing the model to memorize wrong labels. Both biases accumulate over training and degrade performance. To mitigate these issues, we propose Marginal Distribution Adjustment (MDA) and Candidate Class Selection (CCS). MDA dynamically reshapes the model's predicted class distribution toward uniformity, ensuring more fair sample selection across classes. CCS leverages training dynamics to identify likely true labels and removes them from the classification task, preventing memorization of incorrect annotations while converting weakly related labels into useful supervision. Both MDA and CCS are plug-and-play modules. Extensive experiments show that integrating MDA and CCS into either existing sample selectors or advanced LNL pipeline consistently enhances performance on both CIFAR-10/100 with synthetic noise and real-world datasets (CIFAR-N, Clothing1M, WebVision), demonstrating their broad applicability in LNL methods. The code is available at https://github.com/Aliinton/DSS.
SpiralDiff: Spiral Diffusion with LoRA for RGB-to-RAW Conversion Across Cameras
Authors: Huanjing Yue; Shangbin Xie; Cong Cao; Qian Wu; Lei Zhang; Lei Zhao; Jingyu Yang
Venue: CVPR 2026 (main)

Abstract: RAW images preserve superior fidelity and rich scene information compared to RGB, making them essential for tasks in challenging imaging conditions. To alleviate the high cost of data collection, recent RGB-to-RAW conversion methods aim to synthesize RAW images from RGB. However, they overlook two key challenges: (i) the reconstruction difficulty varies with pixel intensity, and (ii) multi-camera conversion requires camera-specific adaptation. To address these issues, we propose SpiralDiff, a diffusion based framework tailored for RGB-to-RAW conversion with a signal-dependent noise weighting strategy that adapts reconstruction fidelity across intensity levels. In addition, we introduce CamLoRA, a camera-aware lightweight adaptation module that enables a unified model to adapt to different camera-specific ISP characteristics. Extensive experiments on four benchmark datasets demonstrate the superiority of SpiralDiff in RGB-to-RAW conversion quality and its downstream benefits in RAW-based object detection. Our code is available at https://github.com/Chuancy-TJU/SpiralDiff.