2 citations · 2 across the 12 of their papers we have counts for
13 papers
LYRIC: Language-Driven Physics-Based Character Control for Contact-Rich Whole-Body Object Interaction
Zeyu Han, Zichong Meng, Julian Tanke +8
We present LYRIC, a generative flow-matching controller for language-driven physics-based contact-rich interaction control, that enables simulated characters to perform contact-ric…
Omni-Interactive Universal Embedder
Wei-Yao Wang, Kazuya Tateishi, Shuyang Cui +4
Multimodal representation learning has been shifting from traditional two-tower architectures to large language model (LLM)-based embedders due to their strong instruction-followin…
MLLMCLIP: Feature-Level Distillation of MLLM for Robust Vision-Language Representations
Jongsuk Kim, Qiyu Wu, Zhuoyuan Mao +3
Pretrained vision-language models such as CLIP excel at zero-shot recognition but often fail at compositionality, particularly attribute-object and relational structures. Recent st…
DynaVieW: Schema-Guided World Modeling for Understanding Hierarchical Visual Dynamics
Silin Gao, Hao Zhao, Zeming Chen +8
Multimodal LLMs struggle to systematically model the temporal evolution of visual scenes in videos or multi-image sequences. Such inputs require models to predict or simulate multi…
MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation
JoungBin Lee, Jaewoo Jung, Jongmin Lee +8
Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the refere…
4D Reconstruction from Sparse Dynamic Cameras
Kazuki Ozeki, Shun Kenney, Yuto Shibata +6
Although dynamic 3D (i.e., 4D) reconstruction from a monocular dynamic camera has recently advanced, it remains fundamentally limited by depth ambiguity. In this paper, we focus on…
Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
Mungyeom Kim, Minkyeong Jeon, Honggyu An +10
Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame,…
Echoes Over Time: Unlocking Length Generalization in Video-to-Audio Generation Models
Christian Simon, Masato Ishii, Wei-Yao Wang +8
Scaling multimodal alignment between video and audio is challenging, particularly due to limited data and the mismatch between text descriptions and frame-level video information.…
AutoRefiner: Improving Autoregressive Video Diffusion Models via Reflective Refinement Over the Stochastic Sampling Path
Zhengyang Yu, Akio Hayakawa, Masato Ishii +4
Autoregressive video diffusion models (AR-VDMs) show strong promise as scalable alternatives to bidirectional VDMs, enabling real-time and interactive applications. Yet there remai…
Schrodinger Audio-Visual Editor: Object-Level Audiovisual Removal
Weihan Xu, Kan Jen Cheng, Koichi Saito +10
Joint editing of audio and visual content is crucial for precise and controllable content creation. This new task poses challenges due to the limitations of paired audio-visual dat…
Coherent Audio-Visual Editing via Conditional Audio Generation Following Video Edits
Masato Ishii, Akio Hayakawa, Takashi Shibuya +1
We introduce a novel pipeline for joint audio-visual editing that enhances the coherence between edited video and its accompanying audio. Our approach first applies state-of-the-ar…
StereoSync: Spatially-Aware Stereo Audio Generation from Video
Christian Marinoni, Riccardo Fosco Gramaccioni, Kazuki Shimada +3
Although audio generation has been widely studied over recent years, video-aligned audio generation still remains a relatively unexplored frontier. To address this gap, we introduc…
Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview
Eleonora Grassucci, Yuki Mitsufuji, Ping Zhang +1
Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems. Its challenge of extracting semantic information from th…