GenAI4ELab/papercli-papers huggingface dataset:
Enhancing the Security of Visual Speaker Authentication Based on Dynamic Lip-Print Analysis
Authors: Yi He; Lei Yang; Bofan Chen; Shilin Wang
Venue: CVPR 2026 (main)

Abstract: In recent years, face-based authentication methods are gradually replacing traditional methods across various applications, offering enhanced security and user convenience. However, these methods are threatened by the continuously evolving DeepFake techniques. In this paper, a novel Visual Speaker Authentication (VSA) approach based on dynamic lip-prints is proposed to improve system security against diverse attacks. The lip-prints are discriminative viseme segments that capture user's localized speaking habits. By leveraging these dynamic lip-prints, this approach can expand the prompt set without requiring additional user-recordings or model retraining, thereby strengthening resilience to replay attacks. Moreover, a Multi-Layer Dynamic-Enhanced Encoder is introduced to model fine-grained lip dynamics, addressing data scarcity challenges and ensuring robust feature extraction even in scenarios with short temporal spans and limited enrollment data. We have carried out extensive experiments on several datasets and the results have demonstrated the effectiveness of the proposed method in both security enhancement and prompt set scalability.
Ultra Diffusion Poser: Diffusion-Based Human Motion Tracking from Sparse Inertial Sensors and Ranging-based Between-sensor Distances
Authors: Dominik Hollidt; Tommaso Bendinelli; Christian Holz
Venue: CVPR 2026 (main)

Abstract: Methods using inertial measurement units (IMUs) provide a wearable alternative to camera-based motion capture. To mitigate drift from inertial signals, recent sparse inertial pose estimators integrate inter-sensor distances measured by ultra-wideband (UWB) ranging. So far, UWB distances have only been used as an additional input feature, ignoring the physical constraints they impose on sensor positions. However, these distances can also be used to reconstruct the underlying 3D sensor layout, which in turn provides more informative input for pose reconstruction. We propose Ultra Diffusion Poser, a diffusion model that explicitly models these geometric constraints. It includes a Spatial Layout Module that analytically reconstructs the 3D sensor positions from UWB measurements. These sensor positions are used alongside IMU signals and UWB distances as a conditioning signal during diffusion. Still, network predictions can violate inter-sensor distance measurements. To address this, we introduce UWB-Diffusion Guidance, which encourages alignment between predicted poses and measured distances during diffusion sampling. Together, these contributions enable our model to achieve state-of-the-art performance, reducing joint position error by up to 22% over prior work. Code can be found here https://github.com/eth-siplab/UltraDiffusionPoser.
Beyond Mimicry: Learning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations
Authors: Wei-Jin Huang; Yue-Yi Zhang; Yi-Lin Wei; Zhi-Wei Xia; Juantao Tan; Yuan-Ming Li; Zhilin Zhao; Wei-Shi Zheng
Venue: CVPR 2026 (main)

Abstract: Enabling humanoid robots to physically interact with humans is a critical frontier, but progress is hindered by the scarcity of high-quality Human-Humanoid Interaction (HHoI) data. While leveraging abundant Human-Human Interaction (HHI) data presents a scalable alternative, we first demonstrate that standard retargeting fails by breaking the essential contacts. We address this with PAIR (Physics-Aware Interaction Retargeting), a contact-centric, two-stage pipeline that preserves contact semantics across morphology differences to generate physically consistent HHoI data.This high-quality data, however, exposes a second failure: conventional imitation learning policies merely mimic trajectories and lack interactive understanding. We therefore introduce D-STAR (Decoupled Spatio-Temporal Action Reasoner), a hierarchical policy that disentangles when to act from where to act. In D-STAR, Phase Attention (when) and a Multi-Scale Spatial module (where) are fused by the diffusion head to produce synchronized whole-body behaviors beyond mimicry.By decoupling these reasoning streams, our model learns robust temporal phases without being distracted by spatial noise, leading to responsive, synchronized collaboration. We validate our framework through extensive and rigorous simulations, demonstrating significant performance gains over baseline approaches and a complete, effective pipeline for learning complex whole-body interactions from HHI data.
Pluggable Pruning with Contiguous Layer Distillation for Diffusion Transformers
Authors: Jian Ma; Qirong Peng; Xujie Zhu; Peixing Xie; Chen Chen; Haonan Lu
Venue: CVPR 2026 (main)

Abstract: Diffusion Transformers (DiTs) have shown exceptional performance in image generation, yet their large parameter counts incur high computational costs, impeding deployment in resource-constrained settings. To address this, we propose Pluggable Pruning with Contiguous Layer Distillation (PPCL), a flexible structured pruning framework specifically designed for DiT architectures. First, we identify redundant layer intervals through a linear probing mechanism combined with the first-order differential trend analysis of similarity metrics. Subsequently, we propose a plug-and-play teacher-student alternating distillation scheme tailored to integrate depth-wise and width-wise pruning within a single training phase. This distillation framework enables flexible knowledge transfer across diverse pruning ratios, eliminating the need for per-configuration retraining. Extensive experiments on multiple Multi-Modal Diffusion Transformer architecture models demonstrate that PPCL achieves a 50% reduction in parameter count compared to the full model, with less than 3% degradation in key objective metrics. Notably, our method maintains high-quality image generation capabilities while achieving higher compression ratios, rendering it well-suited for resource-constrained environments.
PersonaVLM: Long-Term Personalized Multimodal LLMs
Authors: Chang Nie; Chaoyou Fu; Yifan Zhang; Haihua Yang; Caifeng Shan
Venue: CVPR 2026 (main)

Abstract: Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual preferences remains limited. Prior approaches enable only static, single-turn personalization through input augmentation or output alignment, and thus fail to capture users' evolving preferences and personality over time (see Fig.img1). In this paper, we introduce PersonaVLM, an innovative personalized multimodal agent framework designed for long-term personalization. It transforms a general-purpose MLLM into a personalized assistant by integrating three key capabilities: (a) Remembering: It proactively extracts and summarizes chronological multimodal memories from interactions, consolidating them into a personalized database. (b) Reasoning: It conducts multi-turn reasoning by retrieving and integrating relevant memories from the database.  (c) Response Alignment: It infers the user's evolving personality throughout long-term interactions to ensure outputs remain aligned with their unique characteristics. For evaluation, we establish Persona-MME, a comprehensive benchmark comprising over 2,000 curated interaction cases, designed to assess long-term MLLM personalization across seven key aspects and 14 fine-grained tasks. Extensive experiments validate our method's effectiveness, improving the baseline by 22.4% (Persona-MME) and 9.8% (PERSONAMEM) under a 128k context, while outperforming GPT-4o by 5.2% and 2.0%, respectively. Project page: https://PersonaVLM.github.io.
APPO: Attention-guided Perception Policy Optimization for Video Reasoning
Authors: Henghui Du; Chang Zhou; Xi Chen; Di Hu
Venue: CVPR 2026 (main)

Abstract: Complex video reasoning, actually, relies excessively on fine-grained perception rather than on expert (e.g., Ph.D, Science)-level reasoning.Through extensive empirical observation, we have recognized the critical impact of perception.In particular, when perception ability is almost fixed, enhancing reasoning from Qwen3-8B to OpenAI-o3 yields only 0.7% performance improvement. Conversely, even minimal change in perception model scale (from 7B to 32B) boosts performance by 1.4%, indicating enhancing perception, rather than reasoning, is more critical to improve performance.Therefore, exploring how to enhance perception ability through reasoning without the need for expensive fine-grained annotation information is worthwhile.To achieve this goal, we specially propose APPO, the Attention-guided Perception Policy Optimization algorithm that leverages token-level dense rewards to improve model's fine-grained perception.The core idea behind APPO is to optimize those tokens from different responses that primarily focus on the same crucial video frame (called intra-group perception tokens).Experimental results on diverse video benchmarks and models with different scales (3/7B) demonstrate APPO consistently outperforms GRPO and DAPO (0.5%   4%). We hope our work provides a promising approach to effectively enhance model's perception abilities through reasoning in a low-cost manner, serving diverse scenarios and demands.
An Efficient Token Compression Framework for Visual Object Tracking
Authors: Weijing Wu; Qihua Liang; Bineng Zhong; Haiying Xia; Zhiyi Mo; Shuxiang Song
Venue: CVPR 2026 (main)

Abstract: Refining visual representations by eliminating their internal feature-level redundancy is crucial for simultaneously optimizing the performance and computational cost of models in visual tracking. To enhance their performance, many contemporary Transformer-based trackers leverage a larger number of historical template frames to capture richer spatio-temporal cues. However, this strategy leads to a massive number of input visual tokens. This creates two critical issues: it imposes a quadratic computational burden and can also degrade the tracker's overall performance. To bridge this gap, we propose a compress-then-interact tracking framework, ETCTrack, that learns to efficiently compress template tokens from historical template frames into a robust target representation, moving beyond handcrafted rules. Our method first employs the Adaptive Token Compressor to dynamically construct compact yet highly discriminative template tokens by filtering out redundant visual tokens. These refined template tokens are then processed by our Hierarchical Interaction Encoder to achieve a deep, adaptive interaction with the search features. Refined search features ensure subsequent precise target localization. Experiments on seven benchmarks demonstrate that our method outperforms current state-of-the-art trackers. ETCTrack-B224 reduces the number of template tokens by 60%, leading to a 21.4% reduction in MACs with only a 0.4% drop in accuracy. The source code are available at https://github.com/PJD-WJ/ETCTrack.
GraphVLM: Benchmarking Vision Language Models for Multimodal Graph Learning
Authors: Jiajin Liu; Dongzhe Fan; Chuanhao Ji; Daochen Zha; Qiaoyu Tan
Venue: CVPR 2026 (main)

Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities in aligning and understanding multimodal signals, yet their potential to reason over structured data, where multimodal entities are connected through explicit relational graphs, remains largely underexplored. Unlocking this capability is crucial for real-world applications such as social networks, recommendation systems, and scientific discovery, where multimodal information is inherently structured. To bridge this gap, we present GraphVLM, a systematic benchmark designed to evaluate and harness the capabilities of VLMs for multimodal graph learning (MMGL). GraphVLM investigates three complementary paradigms for integrating VLMs with graph reasoning: (1) VLM-as-Encoder, which enriches graph neural networks through multimodal feature fusion; (2) VLM-as-Aligner, which bridges modalities in latent or linguistic space to facilitate LLM-based structured reasoning; and (3) VLM-as-Predictor, which directly employs VLMs as multimodal backbones for graph learning tasks. Extensive experiments across six datasets from diverse domains demonstrate that VLMs enhance multimodal graph learning via all three roles. Among these paradigms, VLM-as-Predictor achieves the most substantial and consistent performance gains, revealing the untapped potential of vision-language models as a new foundation for multimodal graph learning.
Boundary-Responsive Differentiable Gating for Superpixel-Based Segmentation
Authors: Fatmaelzahraa Ahmed; Zhihe Lu; Gianni Caro; Diram Tabaa; Mohamed Hamdy; Muraam Abdel-Ghani; Abdulaziz Al-Ali; Muhammad Arsalan; Shidin Balakrishnan
Venue: CVPR 2026 (main)

Abstract: We present BRDG, a boundary-responsive differentiable gating superpixel framework designed to resolve the trade-off between computational efficiency and segmentation precision in surgical scenes. At its core, the architecture is organized into three cooperative agents within a fully differentiable backbone. The Region Creator agent converts dense features into learnable superpixel tokens, jointly learning region descriptors and dense context. The Boundary Detector agent acts as a gating mechanism, estimating boundary confidence from region features to predict where refinement is needed. The refinement agent uses this gate to selectively fuse efficient coarse predictions with a high-fidelity refinement path that restores pixel-level details. To further enhance distinctiveness, an adjacency-boosted contrastive loss mines hard negatives across neighboring regions to improve boundary separation. We evaluate BRDG on three surgical tasks requiring high-precision EndoVis18-parts, EndoVis18-tools, EndoVis17-tools, as well as general domain benchmarks. Our model improves mIoU by substantial margins 4.5-7.0 over strong pixel-wise baselines while raising Boundary-F1 scores by over 10 points. Under the same hardware (RTX-A6000 Pro), it reaches 150.25 FPS with only 24M parameters. This makes it 10 times faster and 3.5 smaller than current state-of-the-art models, effectively resolving the critical accuracy-efficiency trade-off in real-time segmentation.
Rethinking Asymmetric Quantization: Hidden Symmetry in Vision Model Weights
Authors: Masafumi Mori; Shinya Gongyo; Mitsuru Ambai
Venue: CVPR 2026 (main)

Abstract: Post-training quantization (PTQ) enables rapid deployment of deep pretrained models. In the low-bit regime, recent PTQ methods for vision models adopt asymmetric quantization (AsymQ), introducing zero-point offsets to mitigate quantization errors. However, these offsets impose substantial hardware overhead and fail to fully capture the non-symmetric structure of pretrained weight distributions, leaving many quantization levels unused.In this paper, we reveal a hidden symmetry in the pretrained weights: after removing a few sparse outliers, the distribution becomes nearly symmetric.Accordingly, we propose Dense and Additive Sparse Quantization (DASQ), which decomposes the weights into dense and sparse matrices.The dense component captures the symmetric structure around zero, while the sparse component models the removed outliers, and both can be processed in parallel and can be implemented with efficient zero-point-free computation.Experiments on image classification, object detection, and instance segmentation show that DASQ surpasses state-of-the-art PTQ methods with lower BOPs. On an FPGA, DASQ also demonstrates higher accuracy and lower power consumption than AsymQ at comparable throughput.
Hierarchical Enhancement of Semantic Priors for Disentangled Text-Driven Motion Generation
Authors: Wenhan Lv; Shaopan Wang; Xiangyu Wu; Tianchu Hang; Zhongquan Jian; Qingqiang Wu
Venue: CVPR 2026 (main)

Abstract: Text-to-motion generation aims to synthesize realistic and semantically aligned 3D human motions from natural language descriptions. Existing diffusion-based methods often rely on isotropic latent priors and shallow cross-modal supervision, which lead to semantic entanglement, limited controllability, and poor interpretability. We propose HESP, a unified diffusion framework that hierarchically enhances semantic priors for disentangled text-driven motion generation. At its core, HESP introduces an Adaptive Gaussian Variational Autoencoder (AG-VAE) that structures the latent motion manifold into multiple semantically coherent submanifolds, enabling interpretable and controllable motion representations. To further bridge linguistic and kinematic semantics, we design a Dynamic Cross-Modal Memory (DCMM) module for adaptive semantic fusion and a Hierarchical Cross-Modal Attention (HCA) mechanism to capture multi-level text-motion correspondences. Extensive experiments on HumanML3D and KIT-ML demonstrate that HESP consistently outperforms state-of-the-art baselines such as SALAD, MoMask, and MDM, achieving improvement while maintaining higher diversity and physical plausibility. Moreover, the structured latent space of HESP provides interpretable clusters that reveal clear semantic boundaries among different motion categories.
MovieRecapsQA: A Multimodal Open-Ended Video Question-Answering Benchmark
Authors: Shaden Shaar; Bradon Thymes; Sirawut Chaixanien; Claire Cardie; Bharath Hariharan
Venue: CVPR 2026 (main)

Abstract: Understanding real-world videos such as movies requires integrating visual and dialogue cues. Yet existing VideoQA benchmarks struggle to capture this multimodal reasoning and, given the difficulty of evaluating free-form answers,  largely resort to simple multiple choice questions. We introduce a novel open-ended multimodal VideoQA benchmark, MovieRecapsQA, created using movie recap videos -- a distinctive type of YouTube content that summarizes a film via a voiceover description of key clips from the movie (recap video). From the transcribed voiceover (recap summary) of 60 recap videos, we generate  8.2K questions along with the necessary "facts" expected in each answer; the former facilitates the creation of questions that require mutimodal reasoning and the latter allow the construction of a reference-free evaluation metric that can be applied to open-ended responses. To our knowledge, this is the first reference-free open-ended VideoQA benchmark. The benchmark allows each question to be evaluated in different input video settings: given (a) the full-length movie, (b) the full ( 11 min) recap video (visual only), (c)  14 min of aligned movie scenes, i.e, movie scenes relevant to the question, and (d)  1.2 min of aligned recap video scenes. In all cases, the text of any associated movie dialogue is provided. Each question is categorized by the modality required to answer it---visual, dialogue, or both---enabling fine-grained evaluation of multimodal capabilities. We benchmark (setting (d)) seven state-of-the-art MLLMs and find that (i) only our reference-free metric produces meaningful human-aligned model separation; (ii) vision-centric questions yield the lowest scores across all models; (iii) removing visual input often improves model factuality; and (iv) the primary bottleneck is visual perception, not visual reasoning.
PhysHead: Simulation-Ready Gaussian Head Avatars
Authors: Berna Kabadayi; Vanessa Sklyarova; Wojciech Zielonka; Justus Thies; Gerard Pons-Moll
Venue: CVPR 2026 (main)

Abstract: Realistic digital avatars require expressive and dynamic hair motion; however, most existing head avatar methods assume rigid hair movement. These methods often fail to disentangle hair from the head, representing it as a simple outer shell and failing to capture its natural volumetric behavior. In this paper, we address these limitations by introducing PhysHead, a hybrid representation for animatable head avatars with realistic hair dynamics learned from multiview video. At the core is a 3D Gaussian-based layered representation of the head. Our approach combines a 3D parametric mesh for the head with strand-based hair, which can be directly simulated using physics engines. For the appearance model, we employ Gaussian primitives attached to both the head mesh and hair segments. This representation enables the creation of photorealistic head avatars with dynamic hair behavior, such as wind-blown motion, overcoming the constraints of rigid hair in existing methods. However, these animation capabilities also require new training schemes. In particular, we propose the use of VLM-based models to generate appearance of regions that are occluded in the dynamic training sequences. In quantitative and qualitative studies, we demonstrate the capabilities of the proposed model and compare it with existing baselines. We show that our method can synthesize physically plausible hair motion besides expression and camera control. For additional results and code, please refer to https://physhead.github.io
Scalable Object Relation Encoding for Better 3D Spatial Reasoning in Large Language Models
Authors: Shengli Zhou; Minghang Zheng; Feng Zheng; Yang Liu
Venue: CVPR 2026 (main)

Abstract: Spatial reasoning focuses on locating target objects based on spatial relations in 3D scenes, which plays a crucial role in developing intelligent embodied agents. Due to the limited availability of 3D scene-language paired data, it is challenging to train models with strong reasoning ability from scratch. Previous approaches have attempted to inject 3D scene representations into the input space of Large Language Models (LLMs) and leverage the pretrained comprehension and reasoning abilities for spatial reasoning. However, models encoding absolute positions struggle to extract spatial relations from prematurely fused features, while methods explicitly encoding all spatial relations (which is quadratic in the number of objects) as input tokens suffer from poor scalability. To address these limitations, we propose QuatRoPE, a novel positional embedding method with an input length that is linear to the number of objects, and explicitly calculates pairwise spatial relations through the dot product in attention layers. QuatRoPE's holistic vector encoding of 3D coordinates guarantees a high degree of spatial consistency, maintaining fidelity to the scene's geometric integrity. Additionally, we introduce the Isolated Gated RoPE Extension (IGRE), which effectively limits QuatRoPE's influence to object-related tokens, thereby minimizing interference with the LLM's existing positional embeddings and maintaining the LLM's original capabilities. Extensive experiments demonstrate the effectiveness of our approaches. The code and data are available at https://github.com/oceanflowlab/QuatRoPE.
Physical Simulator In-the-Loop Video Generation
Authors: Lin Geng Foo; Mark He Huang; Alexandros Lattas; Stylianos Moschoglou; Thabo Beeler; Christian Theobalt
Venue: CVPR 2026 (main)

Abstract: Recent advances in diffusion-based video generation have achieved remarkable visual realism but still struggle to obey basic physical laws such as gravity, inertia, and collision. Generated objects often move inconsistently across frames, exhibit implausible dynamics, or violate physical constraints, limiting the realism and reliability of AI-generated videos. We address this gap by introducing Physical Simulator In-the-loop Video Generation (PSIVG), a novel framework that integrates a physical simulator into the video diffusion process. Starting from a template video generated by a pre-trained diffusion model, PSIVG reconstructs the 4D scene and foreground object meshes, initializes them within a physical simulator, and generates physically consistent trajectories. These simulated trajectories are then used to guide the video generator toward spatio-temporally physically coherent motion. To further improve texture consistency during object movement, we propose a Test-Time Texture Consistency Optimization (TTCO) technique that adapts text and feature embeddings based on pixel correspondences from the simulator. Comprehensive experiments demonstrate that PSIVG produces videos that better adhere to real-world physics while preserving visual quality and diversity. Project Page: https://vcai.mpi-inf.mpg.de/projects/PSIVG