Denoising Diffusion Probabilistic Models for Styled Walking Synthesis
Generating realistic motions for digital humans is time-consuming for many graphics applications. Data-driven motion synthesis approaches have seen solid progress in recent years through deep generative models. These results offer high-quality motions but typically suffer in motion style diversity. For the first time, we propose a framework using the denoising diffusion probabilistic model (DDPM) to synthesize styled human motions, integrating two tasks into one pipeline with increased style diversity compared with traditional motion synthesis methods. Experimental results show that our system can generate high-quality and diverse walking motions.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingDiversityMotion SynthesisMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion Models
Text-to-Image synthesis is the task of generating an image according to a specific text description. Generative Adversarial Networks have been considered the standard method for image synthesis virtually since their intr…
Data AugmentationDenoisingHandwriting generationHTR+4Unifying Human Motion Synthesis and Style Transfer with Denoising Diffusion Probabilistic Models
Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches …
DenoisingMotion SynthesisStyle TransferStyleDrop: Text-to-Image Synthesis of Any Style
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language, and out-of-distribution effects make it hard to synthesize a…
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs
Denoising diffusion probabilistic models (DDPMs) are expressive generative models that have been used to solve a variety of speech synthesis problems. However, because of their high sampling costs, DDPMs are difficult to…
DenoisingSpeech Synthesistext-to-speechText to SpeechDenoising Diffusion Probabilistic Models
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by trai…
DenoisingDensity EstimationImage Generation