75 citations · 77 across the 10 of their papers we have counts for
10 papers
4Real-Video: Learning Generalizable Photo-Realistic 4D Video Diffusion
Chaoyang Wang, Peiye Zhuang, Tuan Duc Ngo +7
We propose 4Real-Video, a novel framework for generating 4D videos, organized as a grid of video frames with both time and viewpoint axes. In this grid, each row contains frames sh…
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…
Hierarchical Patch Diffusion Models for High-Resolution Video Generation
Ivan Skorokhodov, Willi Menapace, Aliaksandr Siarohin +1
Diffusion models have demonstrated remarkable performance in image and video synthesis. However, scaling them to high-resolution inputs is challenging and requires restructuring th…
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…
Continual Zero-Shot Learning through Semantically Guided Generative Random Walks
Wenxuan Zhang, Paul Janson, Kai Yi +2
Learning novel concepts, remembering previous knowledge, and adapting it to future tasks occur simultaneously throughout a human's lifetime. To model such comprehensive abilities,…