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首次观测 · 记录 #1CVF · CVPR 2026:cvf:a94b8407e5ea21c33d0cbdb6627aa918CVF · CVPR 2026

M4Human: A Large-Scale Multimodal mmWave Radar Benchmark for Human Mesh Reconstruction

Fan, Junqiao, Zhou, Yunjiao, Yang, Yizhuo, Cui, Xinyuan, Zhang, Jiarui, Xie, Lihua, Yang, Jianfei, Lu, Chris Xiaoxuan 等 9 位作者

原文摘要

Human mesh reconstruction (HMR) provides direct insights into body-environment interaction, enabling various immersive applications. However, existing large-scale HMR benchmarks largely rely on line-of-sight RGB sensing, causing HMR systems to inherit the limitations of vision-based systems, including sensitivity to occlusion, lighting variation, and privacy concerns. These limitations have motivated growing interest in radio-frequency (RF) mmWave radar as a privacy-preserving and robust modality for human sensing. Despite this promise, current radar datasets remain limited by sparse skeleton annotations, small scale, and simple in-place actions. To address this gap, we introduce M4Human, the largest-scale multimodal benchmark to date for radar-based HMR, featuring 661K frames---9 times larger than the previous largest---with high-resolution mmWave radar, RGB, and depth data. M4Human provides both raw radar tensors (RT) and processed radar point clouds (RPC), enabling research across different levels of RF signal granularity. It also includes high-quality motion capture (MoCap) annotations with 3D meshes and global trajectories, covering 20 subjects and 50 diverse actions, including in-place, seated, and free-space sports or rehabilitation movements. We establish benchmarks on RT and RPC modalities, as well as multimodal fusion with RGB-D inputs. Extensive results demonstrate the value of M4Human for radar-based human modeling while revealing persistent challenges under fast and unconstrained motion. Our benchmark is publicly available at \href https://fanjunqiao.github.io/M4Human-site/ https://fanjunqiao.github.io/M4Human-site/ .

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:c286294e3c7ea0c01a13a24c616d82f9CVF · CVPR 2026

When Robots Should Say ''I Don't Know'': Benchmarking Abstention in Embodied Question Answering

Wu, Tao, Zhou, Chuhao, Zhao, Guangyu, Cao, Haozhi, Pu, Yewen, Yang, Jianfei

原文摘要

Embodied Question Answering (EQA) requires an agent to interpret language, perceive its environment, and navigate within 3D scenes to produce responses. Existing EQA benchmarks assume that every question must be answered, but embodied agents should know when they do not have sufficient information to answer. In this work, we focus on a minimal requirement for EQA agents, abstention: knowing when to withhold an answer. From an initial study of 500 human queries, we find that 32.4% contain missing or underspecified context. Drawing on this initial study and cognitive theories of human communication errors, we derive five representative categories requiring abstention: actionability limitation, referential underspecification, preference dependence, information unavailability, and false presupposition. We augment OpenEQA by having annotators transform well-posed questions into ambiguous variants outlined by these categories. The resulting dataset, AbstainEQA, comprises 1,636 annotated abstention cases paired with 1,636 original OpenEQA instances for balanced evaluation. Evaluating on AbstainEQA, we find that even the best frontier model only attains 42.79% abstention recall, while humans achieve 91.17%. We also find that scaling, prompting, and reasoning only yield marginal gains, and that fine-tuned models overfit to textual cues. Together, these results position abstention as a fundamental prerequisite for reliable interaction in embodied settings and as a necessary basis for effective clarification.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d19474a64604c2502d8dbc28b3ef7110CVF · CVPR 2026

HiCoGen: Hierarchical Compositional Text-to-Image Generation in Diffusion Models via Reinforcement Learning

Yang, Hongji, Zhou, Yucheng, Han, Wencheng, Tao, Runzhou, Qiu, Zhongying, Yang, Jianfei, Shen, Jianbing

原文摘要

Recent advances in diffusion models have demonstrated impressive capability in generating high-quality images for simple prompts. However, when confronted with complex prompts involving multiple objects and hierarchical structures, existing models struggle to accurately follow instructions, leading to issues such as concept omission, confusion, and poor compositionality. To address these limitations, we propose a Hierarchical Compositional Generative framework (HiCoGen) built upon a novel Chain of Synthesis (CoS) paradigm. Instead of monolithic generation, HiCoGen first leverages a Large Language Model (LLM) to decompose complex prompts into minimal semantic units. It then synthesizes these units iteratively, where the image generated in each step provides crucial visual context for the next, ensuring all textual concepts are faithfully constructed into the final scene. To further optimize this process, we introduce a reinforcement learning (RL) framework. Crucially, we identify that the limited exploration of standard diffusion samplers hinders effective RL. We theoretically prove that sample diversity is maximized by concentrating stochasticity in the early generation stages and, based on this insight, propose a novel Decaying Stochasticity Schedule to enhance exploration. Our RL algorithm is then guided by a hierarchical reward mechanism that jointly evaluates the image at the global, subject, and relationship levels. We also construct HiCoPrompt, a new text-to-image benchmark with hierarchical prompts for rigorous evaluation. Experiments show our approach significantly outperforms existing methods in both concept coverage and compositional accuracy.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:d7c83f43892189d4f75427a018175306CVF · CVPR 2026

DriveVLN: Towards Mapless Vision-and-Language Navigation in Autonomous Driving

Guo, Dongqian, Wei, Haoran, Han, Wencheng, Tao, Runzhou, Qiu, Zhongying, Yang, Jianfei, Shen, Jianbing

原文摘要

Autonomous driving has made substantial progress recently, achieving reliable performance in most real-world environments. However, existing algorithms still depend heavily on high-definition maps, making them ineffective in mapless scenarios such as indoor parking lots. These limitations hinder seamless point-to-point navigation and restrict the broader deployment of the autonomous driving system.To address this challenge, we propose DriveVLN, a new task that extends Vision-and-Language Navigation (VLN) to autonomous driving. DriveVLN employs visual and linguistic priors to guide vehicles toward destinations based solely on concise natural-language descriptions, without access to predefined maps or routes. Unlike conventional VLN, which relies on detailed step-wise instructions in indoor environments, DriveVLN requires models to produce navigation information based on diverse visual cues and history, including signs, landmarks, and textual indicators.We further develop a CARLA-based simulation engine comprising over 200 realistic scenes reconstructed from real road scans, enabling large-scale training and closed-loop evaluation. A baseline model is established through supervised fine-tuning on real data, followed by reinforcement learning in simulation.Comprehensive experiments show that DriveVLN effectively bridges map-based and mapless driving, providing a new foundation for unified, language-driven autonomous navigation in complex real-world environments.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗