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20232026
most citedEnhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview

2 citations · 2 across the 12 of their papers we have counts for

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cs.RO2026

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…

cs.AI2026

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…

cs.CV2026

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…

cs.LG2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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,…

cs.CV2026

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.…