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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2683184dee4ae26bd4053169044fcadfCVF · CVPR 2026

D3D-VLP: Dynamic 3D Vision-Language-Planning Model for Embodied Grounding and Navigation

Wang, Zihan, Lee, Seungjun, Dai, Guangzhao, Lee, Gim Hee

原文摘要

Embodied agents face a critical dilemma that end-to-end models lack interpretability and explicit 3D reasoning, while modular systems ignore cross-component interdependencies and synergies. To bridge this gap, we propose the Dynamic 3D Vision-Language-Planning Model (D3D-VLP). Our model introduces two key innovations: 1) A Dynamic 3D Chain-of-Thought (3D CoT) that unifies planning, grounding, navigation, and question answering within a single 3D-VLM and CoT pipeline; 2) A Synergistic Learning from Fragmented Supervision (SLFS) strategy, which uses a masked autoregressive loss to learn from massive and partially-annotated hybrid data. This allows different CoT components to mutually reinforce and implicitly supervise each other. To this end, we construct a large-scale dataset with 10M hybrid samples from 5K real scans and 20K synthetic scenes that are compatible with online learning methods such as RL and DAgger. Our D3D-VLP achieves state-of-the-art results on multiple benchmarks, including Vision-and-Language Navigation (R2R-CE, REVERIE-CE, NavRAG-CE), Object-goal Navigation (HM3D-OVON), and Task-oriented Sequential Grounding and Navigation (SG3D). Real-world mobile manipulation experiments further validate the effectiveness.

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:4ad8e048c395fe9efb71046bab42263bCVF · CVPR 2026

History to Future: Evolving Agent with Experience and Thought for Zero-shot Vision-and-Language Navigation

Dai, Guangzhao, Wang, Shuo, Wang, Zihan, Xie, Guo-Sen, Yang, Yang, Pan, Jinshan, Sun, Qianru, Shu, Xiangbo

原文摘要

Vision-and-Language Navigation in Continuous Environment (VLN-CE) requires an agent to follow language instructions to navigate the target destination. With the advancement of large language models (LLMs), recent efforts have explored adapting them for zero-shot VLN-CE, offering a promising solution in addressing the drawbacks of poor generalization in the training-based paradigm. However, existing LLM-based works primarily perform naive reasoning for decision-making and lack feedback, e.g., reviewing historical errors and predicting future potentials. Consequently, it may suffer from continuous failure for those initial error tasks. In this paper, we rethink LLM-based zero-shot VLN-CE and propose a new paradigm, named EvoNav, to improve the agent's decision-making with future thought and history experience via Future Chain-of-Thought (F-CoT) and History Chain-of-Experience (H-CoE). F-CoT predicts future actions and landmarks as thoughts to assist navigation progress estimation and direction selection, while H-CoE summarizes historical trajectories and scenes as experience to improve navigation decision reliability. Both F-CoT and H-CoE cooperatively evolve the agent's decision-making. Extensive experiments in both the simulator and real-world environments demonstrate the effectiveness of our EvoNav.

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:742814f7817c0c8a42dd36110ce700f8CVF · CVPR 2026

SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training

Wang, Mengmeng, Jiang, Dengyang, Li, Liuzhuozheng, Lin, Yucheng, Shen, Guojiang, Kong, Xiangjie, Liu, Yong, Dai, Guang 等 9 位作者

原文摘要

Denoising-based diffusion transformers, despite their strong generation performance, suffer from inefficient training convergence. Existing methods addressing this issue, such as REPA (relying on external representation encoders) or SRA (requiring dual-model setups), inevitably incur heavy computational overhead during training due to external dependencies. To tackle these challenges, this paper proposes SRA 2, a lightweight intrinsic self-representation alignment framework for efficient diffusion training. SRA 2 leverages off-the-shelf pre-trained Variational Autoencoder (VAE) features: their reconstruction property ensures inherent encoding of visual priors like rich texture details, structural patterns, and basic semantic information. Specifically, SRA 2 aligns the intermediate latent features of diffusion transformers with VAE features via a lightweight projection layer, supervised by a feature alignment loss. This design accelerates training without extra representation encoders or dual-model maintenance, resulting in a simple yet effective pipeline. Extensive experiments demonstrate that SRA 2 improves both generation quality and training convergence speed compared to vanilla diffusion transformers, matches or outperforms state-of-the-art acceleration methods, and incurs merely 4% extra GFLOPs with zero additional cost for external guidance models.

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:7a8d117b3f313db2e94bd95eac32a0f2CVF · CVPR 2026

DynBridge: Bridging Imagination and Control through Interaction Dynamics for Robot Manipulation

Wang, Alex, Dong, Zhiwei, Bai, Qicheng, Zhang, Chenshi, Yi, Yujie, Dai, Guang, Liu, Yong, Wang, Mengmeng

原文摘要

Recent generative models allow robots to generate future visual outcomes for action guidance, yet most still address imagination and control independently, resulting in visually coherent rollouts but physically inconsistent behaviors. While structural priors enhance spatial grounding, these methods remain visually correlation-driven rather than causally informed, overlooking the bidirectional coupling between robot actions and the evolving environment. We formalize the coupling as interaction dynamics, which specify where environmental changes occur and how actions cause them. Based on this formulation, we introduce DynBridge, an end-to-end framework that unifies imagination and control through the shared dynamics representation. Specifically, DynBridge realizes this via three components: (1) an Interaction Dynamics Generator that forecasts interaction dynamics via joint trajectory generation and action prediction; (2) an Action-Conditioned Dynamics Aggregator that integrates dynamics under control signals; and (3) a Dynamics-Guided Action Predictor that leverages the aggregated dynamics to produce executable, context-aware actions. Results demonstrate that DynBridge consistently outperforms prior methods on simulated and real-world benchmarks without external pretraining.

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 ↗