mlsanityData to 7 Oct 2026

Text2Avatar: Text to 3D Human Avatar Generation with Codebook-Driven Body Controllable Attribute

arXiv 2401.00711 ↗First posted 1 Jan 2024cs.CV cs.AIcited by 6

Not on the map. Why?

Abstract (arXiv)

Generating 3D human models directly from text helps reduce the cost and time of character modeling. However, achieving multi-attribute controllable and realistic 3D human avatar generation is still challenging due to feature coupling and the scarcity of realistic 3D human avatar datasets. To address these issues, we propose Text2Avatar, which can generate realistic-style 3D avatars based on the coupled text prompts. Text2Avatar leverages a discrete codebook as an intermediate feature to establish a connection between text and avatars, enabling the disentanglement of features. Furthermore, to alleviate the scarcity of realistic style 3D human avatar data, we utilize a pre-trained unconditional 3D human avatar generation model to obtain a large amount of 3D avatar pseudo data, which allows Text2Avatar to achieve realistic style generation. Experimental results demonstrate that our method can generate realistic 3D avatars from coupled textual data, which is challenging for other existing methods in this field.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 15 of its 20 references that are in the map. First posted on arXiv 1 Jan 2024.

Builds on

15 of 20 references.

References · 15

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,006
  3. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 691
  4. 2021CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fieldscited by 369
  5. 2020StyleFlow: Attribute-conditioned Exploration of StyleGAN-Generated Images using Conditional Continuous Normalizing Flowscited by 348
  6. 2023Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videoscited by 312
  7. 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
  8. 2019Self-Correction for Human Parsingcited by 179
  9. 2023DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Modelscited by 154
  10. 2023Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generationcited by 133
  11. 2022EVA3D: Compositional 3D Human Generation from 2D Image Collectionscited by 111
  12. 2023TeCH: Text-guided Reconstruction of Lifelike Clothed Humanscited by 104
3 more
  1. 2022FineDance: A Fine-grained Choreography Dataset for 3D Full Body Dance Generationcited by 87
  2. 2022Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translationcited by 73
  3. 2023Consistent123: One Image to Highly Consistent 3D Asset Using Case-Aware Diffusion Priorscited by 64

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2024Instant 3D Human Avatar Generation using Image Diffusion Modelscited by 14
  2. 2024BAD: Bidirectional Auto-regressive Diffusion for Text-to-Motion Generationcited by 12
  3. 2024Novel Object Synthesis via Adaptive Text-Image Harmonycited by 12
  4. 2025SIGMAN:Scaling 3D Human Gaussian Generation with Millions of Assetscited by 12
  5. 2025HuGeDiff: 3D Human Generation via Diffusion with Gaussian Splattingcited by 1
  6. 2025RMLer: Synthesizing Novel Objects across Diverse Categories via Reinforcement Mixing Learningcited by 1