A self-supervised diffusion framework with a low-bitrate vector-quantization bottleneck learns disentangled motion and content latents supporting motion transfer and auto-regressive generation.
Elucidating the design space of diffusion-based generative models.Advances in Neural Information Processing Sys- tems, 35:26565–26577, 2022
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Bitrate-Controlled Diffusion for Disentangling Motion and Content in Video
A self-supervised diffusion framework with a low-bitrate vector-quantization bottleneck learns disentangled motion and content latents supporting motion transfer and auto-regressive generation.