title: DreamHuman: Animatable 3D Avatars from Text
summary: We present DreamHuman, a method to generate realistic animatable 3D human
  avatar models solely from textual descriptions. Recent text-to-3D methods have
  made considerable strides in generation, but are still lacking in important
  aspects. Control and often spatial resolution remain limited, existing methods
  produce fixed rather than animated 3D human models, and anthropometric
  consistency for complex structures like people remains a challenge. DreamHuman
  connects large text-to-image synthesis models, neural radiance fields, and
  statistical human body models in a novel modeling and optimization framework.
  This makes it possible to generate dynamic 3D human avatars with high-quality
  textures and learned, instance-specific, surface deformations. We demonstrate
  that our method is capable to generate a wide variety of animatable, realistic
  3D human models from text. Our 3D models have diverse appearance, clothing,
  skin tones and body shapes, and significantly outperform both generic
  text-to-3D approaches and previous text-based 3D avatar generators in visual
  fidelity. For more results and animations please check our website at
  https://dream-human.github.io.
paper_authors._id: 648c00c8f11318e0a44bcc79
paper_authors.name: Nikos Kolotouros
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-19T07:15:43.820Z
paper_authors._id: 648cd29f8f46d7284544083b
paper_authors.avatarUrl: /avatars/53163398f1a629b9838548b808af53f7.svg
paper_authors.fullname: Nikos Kolotouros
paper_authors.type: user
paper_authors.user: kolotouros
paper_authors._id: 648c00c8f11318e0a44bcc7a
paper_authors.name: Thiemo Alldieck
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-16T17:38:24.141Z
paper_authors._id: 648c964d39d2584ee47af19c
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/648c964d39d2584ee47af19c/5UEkzDTMY8I3svKjR7kxN.jpeg
paper_authors.fullname: Thiemo Alldieck
paper_authors.type: user
paper_authors.user: thiemoall
paper_authors._id: 648c00c8f11318e0a44bcc7b
paper_authors.name: Andrei Zanfir
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-19T07:15:48.199Z
paper_authors._id: 648cd24dab8e3024f5bbe93b
paper_authors.avatarUrl: /avatars/c8bd4b51155cd37fb7e76c97b843d461.svg
paper_authors.fullname: Andrei Zanfir
paper_authors.type: user
paper_authors.user: Andreiz
paper_authors._id: 648c00c8f11318e0a44bcc7c
paper_authors.name: Eduard Gabriel Bazavan
paper_authors._id: 648c00c8f11318e0a44bcc7d
paper_authors.name: Mihai Fieraru
paper_authors._id: 648c00c8f11318e0a44bcc7e
paper_authors.name: Cristian Sminchisescu
paper_title: DreamHuman: Animatable 3D Avatars from Text
paper_summary: We present DreamHuman, a method to generate realistic animatable 3D human
  avatar models solely from textual descriptions. Recent text-to-3D methods have
  made considerable strides in generation, but are still lacking in important
  aspects. Control and often spatial resolution remain limited, existing methods
  produce fixed rather than animated 3D human models, and anthropometric
  consistency for complex structures like people remains a challenge. DreamHuman
  connects large text-to-image synthesis models, neural radiance fields, and
  statistical human body models in a novel modeling and optimization framework.
  This makes it possible to generate dynamic 3D human avatars with high-quality
  textures and learned, instance-specific, surface deformations. We demonstrate
  that our method is capable to generate a wide variety of animatable, realistic
  3D human models from text. Our 3D models have diverse appearance, clothing,
  skin tones and body shapes, and significantly outperform both generic
  text-to-3D approaches and previous text-based 3D avatar generators in visual
  fidelity. For more results and animations please check our website at
  https://dream-human.github.io.
paper_ai_summary:  DreamHuman generates realistic animatable 3D human avatars using text by integrating text-to-image synthesis, neural radiance fields, and statistical human body models.
paper_ai_keywords: text-to-3D methods neural radiance fields statistical human body models animatable 3D human models high-quality textures instance-specific surface deformations visual fidelity