A camera-controlled video world model must keep a scene consistent when the camera returns to it. Memories that render stored features into the target frame can turn depth and pose errors into ghosting or duplicated structure. Positional transport avoids that resampling by relocating a historical patch's address rather than its content, but assigns full positional precision even to inaccurate projections. We present GLIMPSE, a global-local memory interface with band-wise multi-precision coordinates and geometry-conditioned spectral attenuation to reduce sensitivity to inaccurate projected addresses. Local patch memory supplies view-specific appearance, while recurrent global memory provides scene context. Footprint-guided retrieval selects historical patches under a fixed local read budget. Under matched retrieval and read budgets, GLIMPSE improves geometric correspondence and photometric consistency over deterministic positional transport.
Chunyu Qiang Tianjin University and Kuaishou- 快手科技, Xiaopeng Wang, Kang Yin University of Science and Technology of China, Yuzhe Liang Shanghai Jiaotong University, Yuxin Guo Tencent PCG and Institute of Automation, Chinese Academy of Sciences, Teng Ma, Ziyu Zhang, Tianrui Wang 等 13 位作者
Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstructured acoustic textures (sound effects). In this paper, we introduce UniSonate, a unified flow-matching framework capable of synthesizing speech, music, and sound effects through a standardized, reference-free natural language instruction interface. To reconcile structural disparities, we propose a novel dynamic token injection mechanism that projects unstructured environmental sounds into a structured temporal latent space, enabling precise duration control within a phoneme-driven Multimodal Diffusion Transformer (MM-DiT). Coupled with a multi-stage curriculum learning strategy, this approach effectively mitigates cross-modal optimization conflicts. Extensive experiments demonstrate that UniSonate achieves state-of-the-art performance in instruction-based TTS (WER 1.47%) and TTM (SongEval Coherence 3.18), while maintaining competitive fidelity in TTA. Crucially, we observe positive transfer, where joint training on diverse audio data significantly enhances structural coherence and prosodic expressiveness compared to single-task baselines.
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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
Deng, Yinan, Hu, Kejia, Chen, Ye, Dou, Jianyu, Wang, Jiahui, Zhao, Jingyu, Ao, Haojia, Yang, Yi 等 9 位作者
Scalable robot learning is hindered by the high cost of acquiring diverse, high-quality embodied data. Existing data generation approaches partially mitigate this issue but typically depend on hard-to-access hardware and labor-intensive manual effort, with limited generalization to diverse scene configurations. To overcome these limitations, we propose Video2Robo, a framework that generates high-quality and diverse robot data directly from a single human demonstration video, enabling seamless deployment on physical robots. At its core, Video2Robo leverages 3D Gaussian Splatting (3DGS) as a powerful scene representation, enabling high-fidelity rendering and explicit 3D scene editing. The framework tracks temporally consistent motion trajectories of task-relevant objects from raw video footage and identifies key task skills, guiding robots to execute tasks kinematically plausibly under novel object arrangements. Furthermore, by augmenting backgrounds, textures, lighting, and camera views, Video2Robo further enhances the diversity of generated data. Extensive evaluations in both simulation and real-world environments demonstrate that policies trained on Video2Robo data achieve superior generalization and transfer performance. Project webpage: Video2Robo.
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cs.CV
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表