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版本更新 · v3arXiv:2511.18127

Predictive Representation Learning for Manipulation via Streaming Egocentric 3D Hand Forecasting

Ruicong Liu, Yifei Huang, Liangyang Ouyang, Caixin Kang, Yoichi Sato

原文摘要

Learning embodied manipulation from egocentric human data offers a scalable path, reducing the cost of collecting robot-specific demonstrations. However, existing representation learning methods largely stop at perceptual understanding, such as extracting what a scene contains or what an action is. A capable embodied agent is more than understanding: it predicts the next motion. We propose that forecasting 3D human hand motion is one suitable pretraining task, as the representation jointly learns how to manipulate objects and how the motion will unfold next. Building on this hypothesis, we introduce SFHand, the first representation learning method via streaming egocentric 3D hand forecasting. SFHand learns by autoregressively predicting future 3D hand states, including hand type, 2D bounding box, 3D pose, and trajectory, from a continuous stream of video and language instructions. Its autoregressive architecture is equipped with an ROI-enhanced memory layer, capturing temporal context while focusing on salient hand-centric regions. To enable this research, we also introduce SFHand, the first open-source large-scale dataset pairing synchronized 3D hand poses with fine-grained language instructions (247K clips, 3.95M frames). Transferred to downstream embodied control, the learned representation shows state-of-the-art performance on both simulation and real-world manipulation benchmarks, improving success rate by up to 13.4%. Our method also achieves leading 3D hand forecasting accuracy, with 35.8% lower ADE than baseline methods. These results suggest that forecasting hand motion is a better pretraining task for representation learning in embodied manipulation. Projec page: https://ruicongliu.github.io/sfhand/.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06685

Sekai: Incremental Event Memory for Generalized Reasoning Video Segmentation

Zhengtao Liu, Jinrong Xu, Shiu-Hong Kao, Huixin Sun, Yicong Li

原文摘要

Online reasoning video segmentation (Online RVS) requires a model to segment query-referred objects as a video streams in, using only the frames observed so far. Existing formulations largely assume a fixed target set throughout the video, overlooking a fundamental property of streaming reasoning: as new events occur, the objects that satisfy a query may change over time. To address this, we propose a new task, Generalized Reasoning Video Segmentation (GRVS), where the target set depends on query-relevant events accumulated over the observed video history. As new evidence accumulates, the target set can evolve from empty to one or multiple objects, requiring a model to continuously maintain target validity while localizing the valid objects currently in view. To evaluate GRVS, we build Isekai, a diagnostic benchmark of 60 videos and 197 queries across 11 temporal query families, with 12,569 scoring points whose target sets are causally annotated over time. We further propose Sekai, an online framework with two complementary components: an Event Memory Reasoner (EMR) that maintains query-relevant events and evolving target states in structured memory, and a Memory-Grounded Decoder (MGD) that identifies valid targets from this memory and localizes them in the current frame. Experiments show that Sekai significantly outperforms existing online RVS methods, achieving 50.33 J&F and improving over the strongest baseline by 3.2 points. Code, example annotations, and evaluation scripts are available at https://github.com/zenastangbi/Sekai.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2603.12696

HaltNav: Reactive Visual Halting over Lightweight Topological Priors for Robust Vision-Language Navigation

Zihui Yu, Pingcong Li, Bichi Zhang, Sören Schwertfeger

原文摘要

Vision-and-Language Navigation (VLN) is shifting from rigid, step-by-step instruction following toward open-vocabulary, goal-oriented autonomy. Achieving this transition without exhaustive routing prompts requires agents to leverage structural priors. While prior work often assumes computationally heavy 2D/3D metric maps, we instead exploit a lightweight, text-based osmAG (OpenStreetMap Area Graph), a floorplan-level topological representation that is easy to obtain and maintain. However, global planning over a prior map alone is brittle in real-world deployments, where local connectivity can change (e.g., closed doors or crowded passages), leading to execution-time failures. To address this gap, we propose a hierarchical navigation framework HaltNav that couples the robust global planning of osmAG with the local exploration and instruction-grounding capability of VLN. Our approach features an MLLM-based brain module, which is capable of high-level task grounding and obstruction awareness. Conditioned on osmAG, the brain converts the global route into a sequence of localized execution snippets, providing the VLN executor with prior-grounded, goal-centric sub-instructions. Meanwhile, it detects local anomalies via a mechanism we term Reactive Visual Halting (RVH), which interrupts the local control loop, updates osmAG by invalidating the corresponding topology, and triggers replanning to orchestrate a viable detour. To train this halting capability efficiently, we introduce a data synthesis pipeline that leverages generative models to inject realistic obstacles into otherwise navigable scenes, substantially enriching hard negative samples. Extensive experiments demonstrate that our hierarchical framework outperforms several baseline methods without tedious language instructions, and significantly improves robustness for long-horizon vision-language navigation under environmental changes.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39160

CLaW: Codec-Guided Adaptive Latent Watermarking for Traceable Diffusion Image Generation

Huangchao Song, Lingyun Yu, Cong Li, Peiqi Jiang, Mingqi Fang, Hongtao Xie

原文摘要

With the rapid advancement of text-to-image diffusion models, increasingly realistic AI-generated content has raised serious concerns about misuse and copyright infringement. Digital watermarking offers a promising solution by enabling user-level traceability, yet existing methods remain difficult to deploy at scale, hindered by costly model retraining or detection, poor effectiveness-fidelity trade-offs, and limited robustness to image transformations. To address these challenges, we propose CLaW (Codec-guided Latent Watermarking), a robust and efficient watermarking framework for traceable diffusion image generation with frozen diffusion backbones and inversion-free detection. Specifically, CLaW first uses a pretrained watermark codec to map each watermark message to a latent residual, enabling low-cost sampling-time injection. Then, the residual is injected within a late denoising window, where image semantics are better preserved and the watermark signal is less perturbed by subsequent denoising updates, yielding a better balance between watermark effectiveness and image fidelity. Furthermore, we introduce a decoder-guided adaptive injection mechanism that uses decoding confidence as feedback to dynamically calibrate watermark strength, reinforcing weak watermark signals and improving robustness under image transformations. Extensive experiments show that CLaW preserves high visual fidelity and achieves state-of-the-art robustness under image transformations, enabling reliable user-level traceability.

cs.CV · cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.31466

AnyGameRenderer: Entity-Consistent Generative Rendering Across Games and an Entity-Level Benchmark

Ruicong Liu, Liangyang Ouyang, Fengbo Lan, Zhixiang Wang, Kaipeng Zhang

原文摘要

AI coding agents can now build small playable games in minutes, but the results usually look like programmer art: gray blockouts, colored geometric primitives, and flat semantic blobs. We present AnyGameRenderer, a video-to-video generative renderer that turns such footage into high-quality visuals with characters, motion detail, and effects. Throughout, it keeps the layout, camera, and every entity's position and timing intact. Our AnyGameRenderer generalizes across a wide range of genres that agents build, such as tower defense, auto-battlers, top-down survivors, turn-based battles, kart racing, and crowd runners. AnyGameRenderer takes plain video as its game-state input, with no complex representations such as engine G-buffers, per-object class labels, or low-poly proxies. Gray blockouts, colored primitives, semantic maps, and wireframes all work as input. It also preserves entity correspondence: group counts stay correct, and entities appear and disappear on the right frame. We implement AnyGameRenderer by LoRA fine-tuning MiniMax-H3 ref2va, a 33B reference-video-conditioned video generation model. Training uses ~10k paired clips from three sources: (i) multi-stream rendering data from 38 agent-made games; (ii) two-stream rendering data from Unreal Engine, with programmatically controlled crowds, count changes, and entity scales; and (iii) real gameplay recordings, with pairs reconstructed via 3D-box detection. To evaluate the entity correspondence, we propose an entity-level benchmark of 59 held-out cases with 753 entities, scored against ground-truth boxes. Compared with the base model, AnyGameRenderer better preserves entity correspondence: temporal match rises from 50.9% to 62.9%, missed count changes fall from 34.7% to 21.3%, and fewer entities are hallucinated or left unrendered. Project page: https://alaya-lab.github.io/AnyGameRenderer.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1904ACL Anthology · ACL 2026

Beyond Static Alignment: Adaptive Arbitration for Semantic Incongruence in Semi-Supervised Multimodal Sentiment Analysis

Huicong Li, Xiangbo Ji, Wei Wu Inner Mongolia University

原文摘要

Multimodal sentiment analysis is fundamentally challenged by semantic incongruence, where ambiguous visual signals often conflict with explicit textual cues. In semi-supervised scenarios, naively fusing such noisy features contaminates the joint representation, while conventional static alignment strategies fail to effectively arbitrate conflicting modalities in this task, leading to error reinforcement during self-training. To this end, we propose a novel Adaptive Arbitration for Semantic Incongruence (A2SI) framework for semi-supervised multimodal sentiment analysis, which emphasizes stable cross-modal representations and reliable supervision. Specifically, we first constrain unreliable visual representations by leveraging the reliable textual modality as an anchor to align divergent embeddings and reduce representation noise. Based on this, we further consider the reliability of supervision signals and calibrate pseudo-labels by adaptively weighting evidentiary confidence from heterogeneous views. Finally, to prevent error accumulation caused by unreliable samples, we introduce a progressive arbitration mechanism that verifies pseudo-labeled data from dual perspectives, enabling the model to dynamically balance sample diversity and label purity throughout self-training. Extensive experiments on the MVSA-Single and MVSA-Multiple datasets demonstrate that A2SI consistently outperforms state-of-the-art methods under label-limited settings.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表