1 citations · 1 across the 8 of their papers we have counts for
8 papers
Multi-subject Open-set Personalization in Video Generation
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace +7
Video personalization methods allow us to synthesize videos with specific concepts such as people, pets, and places. However, existing methods often focus on limited domains, requi…
Mind the Time: Temporally-Controlled Multi-Event Video Generation
Ziyi Wu, Aliaksandr Siarohin, Willi Menapace +5
Real-world videos consist of sequences of events. Generating such sequences with precise temporal control is infeasible with existing video generators that rely on a single paragra…
VIMI: Grounding Video Generation through Multi-modal Instruction
Yuwei Fang, Willi Menapace, Aliaksandr Siarohin +5
Existing text-to-video diffusion models rely solely on text-only encoders for their pretraining. This limitation stems from the absence of large-scale multimodal prompt video datas…
VIA: Unified Spatiotemporal Video Adaptation Framework for Global and Local Video Editing
Jing Gu, Yuwei Fang, Ivan Skorokhodov +4
Video editing serves as a fundamental pillar of digital media, spanning applications in entertainment, education, and professional communication. However, previous methods often ov…
MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation
Kuan-Chieh Wang, Daniil Ostashev, Yuwei Fang +2
We introduce a new architecture for personalization of text-to-image diffusion models, coined Mixture-of-Attention (MoA). Inspired by the Mixture-of-Experts mechanism utilized in l…
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Tsai-Shien Chen, Aliaksandr Siarohin, Willi Menapace +8
The quality of the data and annotation upper-bounds the quality of a downstream model. While there exist large text corpora and image-text pairs, high-quality video-text data is mu…
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis
Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov +8
Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages, the research community repurposes them to generate videos. Since vide…
AToM: Amortized Text-to-Mesh using 2D Diffusion
Guocheng Qian, Junli Cao, Aliaksandr Siarohin +12
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously. In contrast to existing text-to-3D methods…