InfiniHuman: Infinite 3D Human Creation with Precise Control
Generating realistic and controllable 3D human avatars is a long-standing
challenge, particularly when covering broad attribute ranges such as ethnicity,
age, clothing styles, and detailed body shapes. Capturing and annotating
large-scale human datasets for training generative models is prohibitively
expensive and limited in scale and diversity. The central question we address
in this paper is: Can existing foundation models be distilled to generate
theoretically unbounded, richly annotated 3D human data? We introduce
InfiniHuman, a framework that synergistically distills these models to produce
richly annotated human data at minimal cost and with theoretically unlimited
scalability. We propose InfiniHumanData, a fully automatic pipeline that
leverages vision-language and image generation models to create a large-scale
multi-modal dataset. User study shows our automatically generated identities
are undistinguishable from scan renderings. InfiniHumanData contains 111K
identities spanning unprecedented diversity. Each identity is annotated with
multi-granularity text descriptions, multi-view RGB images, detailed clothing
images, and SMPL body-shape parameters. Building on this dataset, we propose
InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body
shape, and clothing assets. InfiniHumanGen enables fast, realistic, and
precisely controllable avatar generation. Extensive experiments demonstrate
significant improvements over state-of-the-art methods in visual quality,
generation speed, and controllability. Our approach enables high-quality avatar
generation with fine-grained control at effectively unbounded scale through a
practical and affordable solution. We will publicly release the automatic data
generation pipeline, the comprehensive InfiniHumanData dataset, and the
InfiniHumanGen models at https://yuxuan-xue.com/infini-human.
68edc0bbde1fee572713a884
Yuxuan Xue
68edc0bbde1fee572713a885
Xianghui Xie
68edc0bbde1fee572713a886
Margaret Kostyrko
68edc0bbde1fee572713a887
Gerard Pons-Moll
InfiniHuman: Infinite 3D Human Creation with Precise Control
Generating realistic and controllable 3D human avatars is a long-standing
challenge, particularly when covering broad attribute ranges such as ethnicity,
age, clothing styles, and detailed body shapes. Capturing and annotating
large-scale human datasets for training generative models is prohibitively
expensive and limited in scale and diversity. The central question we address
in this paper is: Can existing foundation models be distilled to generate
theoretically unbounded, richly annotated 3D human data? We introduce
InfiniHuman, a framework that synergistically distills these models to produce
richly annotated human data at minimal cost and with theoretically unlimited
scalability. We propose InfiniHumanData, a fully automatic pipeline that
leverages vision-language and image generation models to create a large-scale
multi-modal dataset. User study shows our automatically generated identities
are undistinguishable from scan renderings. InfiniHumanData contains 111K
identities spanning unprecedented diversity. Each identity is annotated with
multi-granularity text descriptions, multi-view RGB images, detailed clothing
images, and SMPL body-shape parameters. Building on this dataset, we propose
InfiniHumanGen, a diffusion-based generative pipeline conditioned on text, body
shape, and clothing assets. InfiniHumanGen enables fast, realistic, and
precisely controllable avatar generation. Extensive experiments demonstrate
significant improvements over state-of-the-art methods in visual quality,
generation speed, and controllability. Our approach enables high-quality avatar
generation with fine-grained control at effectively unbounded scale through a
practical and affordable solution. We will publicly release the automatic data
generation pipeline, the comprehensive InfiniHumanData dataset, and the
InfiniHumanGen models at https://yuxuan-xue.com/infini-human.
InfiniHuman framework distills existing models to generate large-scale, richly annotated 3D human data using a diffusion-based generative pipeline, achieving high visual quality, speed, and controllability.
vision-language models image generation models diffusion-based generative pipeline SMPL body-shape parameters