19 citations · 104 across the 41 of their papers we have counts for
6 papers · 1 filter
Odoriko: A Shape-Aware Multimodal Diffusion Framework for Human Motion
Dongseok Shim, Julian Tanke, Kengo Uchida +5
Human motion generation has been widely studied across diverse input modalities, text, music, and video, and recent efforts have unified these into single multimodal frameworks. Ho…
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.…
TITAN-Guide: Taming Inference-Time AligNment for Guided Text-to-Video Diffusion Models
Christian Simon, Masato Ishii, Akio Hayakawa +4
In the recent development of conditional diffusion models still require heavy supervised fine-tuning for performing control on a category of tasks. Training-free conditioning via g…
Mining Your Own Secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models
Saurav Jha, Shiqi Yang, Masato Ishii +7
Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However,…
MoLA: Motion Generation and Editing with Latent Diffusion Enhanced by Adversarial Training
Kengo Uchida, Takashi Shibuya, Yuhta Takida +4
In text-to-motion generation, controllability as well as generation quality and speed has become increasingly critical. The controllability challenges include generating a motion o…
Visual Echoes: A Simple Unified Transformer for Audio-Visual Generation
Shiqi Yang, Zhi Zhong, Mengjie Zhao +4
In recent years, with the realistic generation results and a wide range of personalized applications, diffusion-based generative models gain huge attention in both visual and audio…