1 citations · 1 across the 3 of their papers we have counts for
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MALT Diffusion: Memory-Augmented Latent Transformers for Any-Length Video Generation
Sihyun Yu, Meera Hahn, Dan Kondratyuk +6
Diffusion models are successful for synthesizing high-quality videos but are limited to generating short clips (e.g., 2-10 seconds). Synthesizing sustained footage (e.g. over minut…
Learning Complex Non-Rigid Image Edits from Multimodal Conditioning
Nikolai Warner, Jack Kolb, Meera Hahn +3
In this paper we focus on inserting a given human (specifically, a single image of a person) into a novel scene. Our method, which builds on top of Stable Diffusion, yields natural…
Photorealistic Video Generation with Diffusion Models
Agrim Gupta, Lijun Yu, Kihyuk Sohn +6
We present W.A.L.T, a transformer-based approach for photorealistic video generation via diffusion modeling. Our approach has two key design decisions. First, we use a causal encod…
VideoPoet: A Large Language Model for Zero-Shot Video Generation
Dan Kondratyuk, Lijun Yu, Xiuye Gu +28
We present VideoPoet, a language model capable of synthesizing high-quality video, with matching audio, from a large variety of conditioning signals. VideoPoet employs a decoder-on…
Text and Click inputs for unambiguous open vocabulary instance segmentation
Nikolai Warner, Meera Hahn, Jonathan Huang +2
Segmentation localizes objects in an image on a fine-grained per-pixel scale. Segmentation benefits by humans-in-the-loop to provide additional input of objects to segment using a…