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- ScaMo: Exploring the Scaling Law in Autoregressive Motion Generation Model63
A scalable motion generation framework that includes the motion tokenizer Motion FSQ-VAE and a text-prefix autoregressive transformer is introduced and the existence of scaling laws within the context of motion generation is confirmed for the first time.
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- GLaMa: Joint Spatial and Frequency Loss for General Image Inpainting31
The proposed GLaMa can better capture different types of missing information by using more types of masks and introduces a frequency-based loss in addition to the traditional spatial reconstruction loss and adversarial loss to reduce the chessboard effect and ripples in the reconstructed image.
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- Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation26
The proposed Hierarchical Diffusion Autoencoders (HDAE) that exploits the coarse-to-fine feature hierarchy for the latent space of diffusion models and proposes a truncated feature based approach for disentangled image manipulation.
- Diffusion Models Need Visual Priors for Image Generation13
This work proposes Diffusion on Diffusion (DoD), an innovative multi-stage generation framework that first extracts visual priors from previously generated samples, then provides rich guidance for the diffusion model leveraging visual priors from the early stages of diffusion sampling.
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