GenAI4ELab/papercli-papers huggingface dataset:
Ego-InBetween: Generating Object State Transitions in Ego-Centric Videos
Authors: Mengmeng Ge; Takashi Isobe; Xu Jia; Yanan Sun; Zetong Yang; Weinong Wang; Dong Zhou; Dong Li; Huchuan Lu; Emad Barsoum
Venue: CVPR 2026 (main)

Abstract: Understanding physical transformation processes is crucial for both human cognition and artificial intelligence systems, particularly from an egocentric perspective, which serves as a key bridge between humans and machines in action modeling. We define this modeling process as Egocentric Instructed Visual State Transition (EIVST), which involves generating intermediate frames that depict object transformations between initial and target states under a brief action instruction. EIVST poses two challenges for current generative models: (1) understanding the visual scenes of the initial and target states and reasoning about transformation steps from an egocentric view, and (2) generating a consistent intermediate transition that follows the given instruction while preserving object appearance across the two visual states. To address these challenges, we propose the EgoIn framework. It first infers the multi-step transition process between two given states using TransitionVLM, fine-tuned on our curated dataset to better adapt to this task and reduce hallucinated information. It then generates a sequence of frames based on transition conditions produced by the proposed Transition Conditioning module. Additionally, we introduce Object-aware Auxiliary Supervision to preserve consistent object appearance throughout the transition. Extensive experiments on human-object and robot-object interaction datasets demonstrate EgoIn's superior performance in generating semantically meaningful and visually coherent transformation sequences.
Unleashing the Power of Chain-of-Prediction for Monocular 3D Object Detection
Authors: Zhihao Zhang; Abhinav Kumar; Girish Chandar Ganesan; Xiaoming Liu
Venue: CVPR 2026 (main)

Abstract: Monocular 3D detection (Mono3D) aims to infer 3D bounding boxes from a single RGB image.Without auxiliary sensors such as LiDAR, this task is inherently ill-posed since the 3D-to-2D projection introduces depth ambiguity.Previous works often predict 3D attributes (e.g., depth, size, and orientation) in parallel, overlooking that these attributes are inherently correlated through the 3D-to-2D projection.However, simply enforcing such correlations through sequential prediction can propagate errors across attributes, especially when objects are occluded or truncated, where inaccurate size or orientation predictions can further amplify depth errors.Therefore, neither parallel nor sequential prediction is optimal.In this paper, we propose MonoCoP, an adaptive framework that learns when and how to leverage inter-attribute correlations with two complementary designs.A Chain-of-Prediction (CoP) explores inter-attribute correlations through feature-level learning, propagation, and aggregation, while an Uncertainty-Guided Selector (GS) dynamically switches between CoP and parallel paradigms for each object based on the predicted uncertainty.By combining their strengths, MonoCoP achieves state-of-the-art (SOTA) performance on KITTI, nuScenes, and Waymo, significantly improving depth accuracy, particularly for distant objects.
RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning
Authors: Ehsan Ahmadi; Hunter Schofield; Behzad Khamidehi; Fazel Arasteh; Jinjun Shan; Lili Mou; Dongfeng Bai; Kasra Rezaee
Venue: CVPR 2026 (main)

Abstract: Supervised open-loop training has been widely adopted for training traffic simulation models; however, it fails to capture the inherently dynamic, multi-agent interactions common in complex driving scenarios. We introduce RLFTSim, a reinforcement-learning-based fine-tuning framework that enhances scenario realism by aligning simulator rollouts with real-world data distributions and provides a method for distilling goal-conditioned controllability in scenario generation. We instantiate RLFTSim on top of a pre-trained simulation model, design a reward that balances fidelity and controllability, and perform comprehensive experiments on the Waymo Open Motion Dataset. Our results show improvements in realism, achieving state-of-the-art performance. Compared with other heuristic search-based fine-tuning methods, RLFTSim requires significantly fewer samples due to a proposed low-variance and dense reward signal, and it directly addresses the realism alignment issue by design. We also demonstrate the effectiveness of our approach for distilling traffic simulation controllability through goal conditioning. Project page is available at https://ehsan-ami.github.io/rlftsim.
Any4D: Unified Feed-Forward Metric 4D Reconstruction
Authors: Jay Karhade; Nikhil Keetha; Yuchen Zhang; Tanisha Gupta; Akash Sharma; Sebastian Scherer; Deva Ramanan
Venue: CVPR 2026 (main)

Abstract: We present Any4D, a scalable multi-view transformer for metric-scale, dense feed-forward 4D reconstruction. Any4D directly generates per-pixel motion and geometry predictions for N frames, in contrast to prior work that typically focuses on either 2-view dense scene flow or sparse 3D point tracking. Moreover, unlike other recent methods for 4D reconstruction from monocular RGB videos, Any4D can process additional modalities and sensors such as RGB-D frames, IMU-based egomotion, and Radar Doppler measurements, when available. One of the key innovations that allows for such a flexible framework is a modular representation of a 4D scene; specifically, per-view 4D predictions are encoded using a variety of egocentric factors (depthmaps and camera intrinsics) represented in local camera coordinates, and allocentric factors (camera extrinsics and scene flow) represented in global world coordinates. We achieve superior performance across diverse setups - both in terms of accuracy (2-3X lower error) and compute efficiency (15X faster) - opening avenues for multiple downstream applications.
It Takes Two: A Duet of Periodicity and Directionality for Burst Flicker Removal
Authors: Lishen Qu; Shihao Zhou; Jie Liang; Hui Zeng; Lei Zhang; Jufeng Yang
Venue: CVPR 2026 (main)

Abstract: Flicker artifacts, arising from unstable illumination and row-wise exposure inconsistencies, pose a significant challenge in short-exposure photography, severely degrading image quality. Unlike typical artifacts, e.g., noise and low-light, flicker is a structured degradation with specific spatial-temporal patterns, which are not accounted for in current generic restoration frameworks, leading to suboptimal flicker suppression and ghosting artifacts. In this work, we reveal that flicker artifacts exhibit two intrinsic characteristics, periodicity and directionality, and propose Flickerformer, a transformer-based architecture that effectively removes flicker without introducing ghosting. Specifically, Flickerformer comprises three key components: a phase-based fusion module (PFM), an autocorrelation feed-forward network (AFFN), and a wavelet-based directional attention module (WDAM). Based on the periodicity, PFM performs inter-frame phase correlation to adaptively aggregate burst features, while AFFN exploits intra-frame structural regularities through autocorrelation, jointly enhancing the network's ability to perceive spatially recurring patterns. Moreover, motivated by the directionality of flicker artifacts, WDAM leverages high-frequency variations in the wavelet domain to guide the restoration of low-frequency dark regions, yielding precise localization of flicker artifacts. Extensive experiments demonstrate that Flickerformer outperforms state-of-the-art approaches in both quantitative metrics and visual quality. The source code is available at https://github.com/qulishen/Flickerformer.
E-comIQ-ZH: A Human-Aligned Dataset and Benchmark for Fine-Grained Evaluation of E-commerce Posters with Chain-of-Thought
Authors: Meiqi Sun; Mingyu Li; Junxiong Zhu
Venue: CVPR 2026 (main)

Abstract: Generative AI is widely used to create commercial posters. However, rapid advances in generation have outpaced automated quality assessment. Existing models emphasize generic esthetics or low level distortions and lack the functional criteria required for e-commerce design. It is especially challenging for Chinese content, where complex characters often produce subtle but critical textual artifacts that are overlooked by existing methods. To address this, we introduce E-comIQ-ZH, a framework for evaluating Chinese e-commerce posters. We build the first dataset E-comIQ-18k to feature multi dimensional scores and expert calibrated Chain of Thought (CoT) rationales. Using this dataset, we train E-comIQ-M, a specialized evaluation model that aligns with human expert judgment. Our framework enables E-comIQ-Bench, the first automated and scalable benchmark for the generation of Chinese e-commerce posters. Extensive experiments show our E-comIQ-M aligns more closely with expert standards and enables scalable automated assessment of e-commerce posters. All datasets, models, and evaluation tools will be released to support future research in this area.
Progressive Mask Distillation for Self-supervised Video Representation
Authors: Kewei Wu; Chong Liang; Zhao Xie; Dan Guo
Venue: CVPR 2026 (main)

Abstract: Masked visual modeling is a self-supervised learning task that does not use visual annotations. It aims to learn discriminative representations via a mask-reconstruction task. A single mask ratio in reconstruction may fail to capture complex semantics, which motivates dynamic masking strategies. In this work, we propose Progressive Mask Distillation (PMD), which utilizes dynamic mask ratios to facilitate progressive semantic learning from easy to hard. PMD integrates three key components: a progressive student distiller, a difficulty-aware region enhancer, and a cross-layer feature aligner. First, to capture dynamic visual semantics, we design a progressive student distiller that trains multiple student models with progressively increasing mask ratios. The early-phase student (with a low mask ratio) learns easy, low-level semantics from more visible tokens. This learned knowledge then guides the next-phase student (with a higher mask ratio) to capture hard, high-level semantics from fewer visible tokens. This progressive distillation mechanism enhances detail reconstruction at a high mask ratio. Second, to alleviate insufficient learning of semantic regions, we design a difficulty-aware region enhancer. We first smooth the region reconstruction loss to reduce large fluctuations across training epochs. The smoothed loss is then used to learn region-level weights, prioritizing accurate learning of regions with large reconstruction losses. Third, to further bridge the semantic gap across network layers, we design cross-layer feature alignment. This module aligns features across shallow, middle, and deep encoder layers, ensuring that shallow-layer features incorporate semantic information from deeper layers. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the Something-Something V2, Kinetics-400, UCF-101, and HMDB-51 datasets.
GThinker: Towards General Multimodal Reasoning via Cue-Guided Rethinking
Authors: Yufei Zhan; Ziheng Wu; Yousong Zhu; Rongkun Xue; Guanghao Zhou; Ruipu Luo; Zhenghao Chen; Can Zhang; Yifan Li; Zhentao He; Zheming Yang; Ming Tang; Minghui Qiu; Jinqiao Wang
Venue: CVPR 2026 (main)

Abstract: Despite recent advances in multimodal reasoning, Multimodal Large Language Models (MLLMs) still struggle on complex tasks where initial visual perceptions can be misleading. This performance gap stems from a critical reasoning flaw we term Visual Inertia: while MLLMs excel at iterative reflection in textual contexts, they tend to uncritically commit to their initial visual interpretations and rarely revise them. To overcome this limitation, we introduce GThinker, an MLLM equipped with a novel adaptive visual rethinking capability. GThinker leverages Cue-Rethinking, a flexible reasoning pattern that not only grounds reasoning in visual cues but also strategically triggers a re-examination of these cues to resolve inconsistencies. To instill this capability, we introduce a novel two-stage training framework. It begins with a pattern-guided cold start, enhanced by a judge-guided selective mechanism to learn from failure cases, followed by incentive reinforcement learning. We further curate the  GThinker-11K dataset to power the training with an iterative multimodal annotation pipeline. Extensive experiments demonstrate that GThinker significantly mitigates visual inertia during reasoning, achieving a leading 81.5% on the M3CoT benchmark, which is rich in such challenges, surpassing the powerful o4-mini model. Furthermore, GThinker shows consistent improvements across a range of multimodal reasoning benchmarks with an average gain of 2.1%, showcasing the broad benefits of equipping MLLMs with the ability to rethink both what they see and how they think. Data, model, and code are available at https://github.com/jefferyZhan/GThinker.
Physically Ground Commonsense Knowledge for Articulated Object Manipulation with Analytic Concepts
Authors: Jiude Wei; Yuxuan Li; Cewu Lu; Jianhua Sun
Venue: CVPR 2026 (main)

Abstract: We humans rely on a wide range of commonsense knowledge to interact with an extensive number and categories of objects in the physical world. Likewise, such commonsense knowledge is also crucial for robots to successfully develop generalized object manipulation skills. While recent advancements in Multi-modal Large Language Models (MLLMs) have showcased their impressive capabilities in acquiring commonsense knowledge and conducting commonsense reasoning, effectively grounding this semantic-level knowledge produced by MLLMs to the physical world to thoroughly guide robots in generalized articulated object manipulation remains a challenge that has not been sufficiently addressed. To this end, we introduce analytic concepts, procedurally defined upon mathematical symbolism that can be directly computed and simulated by machines. By leveraging the analytic concepts as a bridge between the semantic-level knowledge inferred by MLLMs and the physical world where real robots operate, we can identify the knowledge of object structure and functionality with physics-informed representations, and then use the physically grounded knowledge to instruct robot control policies for generalized and accurate articulated object manipulation. Extensive experiments in both real world and simulation demonstrate the superiority of our approach.
Computational Speckle Pattern Interferometry
Authors: Shengxi Wu; Sophia Yang; Dorian Chan; Matthew O'Toole
Venue: CVPR 2026 (main)

Abstract: Visually imperceptible surface deformations encode rich information about a scene, from the mechanical properties of an object to the acoustic vibrations present in the surrounding environment. Optical interferometric techniques can reveal these subtle changes, typically by capturing a sequence of measurements to perform temporal phase shifting. In this paper, we introduce Computational Speckle Pattern Interferometry (CSPI), a novel single-shot approach to estimating per-pixel displacement and motion. Our key insight is that the image formation model for speckle pattern interferometry can be decomposed into spatial and temporal factors, each represented as a vector. After calibrating for the spatial term, we recover the scene dynamics using a reconstruction algorithm modeled after the classic Horn-Schunck method for estimating optical flow. Unlike traditional interferometric methods, CSPI requires no precision instrumentation to perform phase stepping. We demonstrate its effectiveness by measuring per-pixel displacements and motions at sub-micrometer scales, visualizing high-frequency vibrations of a tuning fork and a Chladni plate, and recovering sound indirectly from these vibrations.
CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Models
Authors: Vladislav Pyatov; Gleb Bobrovskikh; Saveliy Galochkin; Nikita Boldyrev; Oleg Voynov; Alexander Filippov; Gonzalo Ferrer; Peter Wonka; Evgeny Burnaev
Venue: CVPR 2026 (main)

Abstract: We introduce CADFS, a data-centric framework that enables large vision-language models to generate complex CAD design histories. Existing generative CAD systems are restricted to sketch-extrude operations due to simplified representations and limited datasets. We address this by introducing a FeatureScript-based representation and constructing a dataset of 450k real-world CAD models spanning 15 modeling operations. We obtain the dataset via a new pipeline that reconstructs clean, executable FeatureScript programs and provides multimodal annotations. Fine-tuning a VLM on this representation yields state-of-the-art results in text-conditioned CAD generation and image-based reconstruction, producing more accurate, diverse, and feature-rich designs than prior frameworks. Ablations show that each individual component of our framework, i.e., the FeatureScript representation, the extended operation set, and representation-aligned textual descriptions, significantly improves performance. Our framework substantially broadens the complexity and realism achievable in generative CAD. The CADFS framework and the new dataset are available at https://voyleg.github.io/cadfs/.
Perceptual Neural Video Compression with Color Separation and Rank Chain
Authors: Xiongzhuang Liang; Chuanbo Tang; Zhuoyuan Li; Li Li; Dong Liu
Venue: CVPR 2026 (main)

Abstract: Neural video compression (NVC) has achieved significant progress in recent years. The state-of-the-art (SOTA) NVC schemes, exemplified by the Deep Conditional Video Coding series, have focused on pursuing higher fidelity (e.g., PSNR), but lack sufficient exploitation of deep networks' advantages for better perceptual quality. We fill in this gap with two new techniques. First, we propose a color-separation-based framework, termed PNVC-C, which decouples luminance and chrominance processing to better align with human visual perception. This framework enables explicit and adaptive allocation of computation and bitrate budgets between luminance and chrominance components. Second, within this framework, we introduce the perceptual optimization scheme Rc-GAN, which leverages a bitrate-based rank chain loss to link variable-rate coding with perceptual quality ranking, enforcing consistent quality ordering and improving perceptual fidelity. Built upon these designs, we establish the PNVC-C framework with two variants: PNVC-C-Base, optimized for objective fidelity, and PNVC-CR, a perceptual variant that applies the Rc-GAN. Experimental results demonstrate that PNVC-C-Base achieves SOTA objective performance in YUV PSNR, while PNVC-CR attains SOTA perceptual quality on LPIPS, DISTS, KID, and FID metrics. The source code is released at https://github.com/lxz-nan/PNVC-CR.
OS-Fed: One Snapshot Is All You Need
Authors: Xuwei Qian; Jinghui Zhang; Yuchuan Tan; Wenbo Huang; Zhen Wu; Shen Zhou; LiSha Gao; Ding Ding; Fang Dong
Venue: CVPR 2026 (main)

Abstract: Reducing communication overhead in federated learning (FL) is challenging but crucial for large-scale distributed privacy-preserving machine learning. Unfortunately, directly compressing model updates often leads to sub-optimal convergence due to information loss, while increasing local computation can cause model divergence. Hence, this paper proposes a drastically different approach that adheres to the maxim that "a picture is worth a thousand words". We observe that the entire gradient information from local training can be effectively reconstructed from a compact, image-like representation. Based on this observation, we propose a novel approach, OS-Fed, which performs One-Shot Federated Learning by transmitting only a single, compact snapshot (comprising an image and a set of learnable labels) per round. To realize this approach, OS-Fed presents new snapshot synthesis techniques to (1) target the accumulated update of a trajectory segment to tackle gradient noise, (2) design a multi-grid snapshot that decouples conflicting gradient directions, and (3) incorporate error compensation to maintain training stability under extreme compression. Extensive experiments on CV and NLP benchmarks show that OS-Fed reduces communication costs by 1.5-16xcompared to state-of-the-art algorithms , resulting in 18-45% faster convergence.
BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation
Authors: Rachit Saluja; Asli Cihangir; Ruining Deng; Johannes C. Paetzold; Fengbei Liu; Mert R. Sabuncu
Venue: CVPR 2026 (main)

Abstract: Segmenting small lesions in medical images remains notoriously difficult. Most prior work tackles this challenge by either designing better architectures, loss functions, or data augmentation schemes; and collecting more labeled data. We take a different view, arguing that part of the problem lies in how the background is modeled. Common lesion segmentation collapses all non-lesion pixels into a single "background" class, ignoring the rich anatomical context in which lesions appear. In reality, the background is highly heterogeneous--composed of tissues, organs, and other structures that can now be labeled manually or inferred automatically using existing segmentation models.In this paper, we argue that training with fine-grained labels that sub-divide the background class, which we call BackSplit, is a simple yet powerful paradigm that can offer a significant performance boost without increasing inference costs. From an information theoretic standpoint, we prove that BackSplit increases the expected Fisher Information relative to conventional binary training, leading to tighter asymptotic bounds and more stable optimization. With extensive experiments across multiple datasets and architectures, we empirically show that BackSplit consistently boosts small-lesion segmentation performance, even when auxiliary labels are generated automatically using pretrained segmentation models. Additionally, we demonstrate that auxiliary labels derived from interactive segmentation frameworks exhibit the same beneficial effect, demonstrating its robustness, simplicity, and broad applicability.