Pith. sign in

REVIEW 8 cited by

AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2306.09864 v1 pith:3IG2CPGC submitted 2023-06-16 cs.CV

Yifei Zeng , Yuanxun Lu , Xinya Ji , Yao Yao , Hao Zhu , Xun Cao This is my paper · ORCID
classification cs.CV
keywords avataravatarboothgenerationimagestextavatarshumanmodel
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We introduce AvatarBooth, a novel method for generating high-quality 3D avatars using text prompts or specific images. Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing. Our key contribution is the precise avatar generation control by using dual fine-tuned diffusion models separately for the human face and body. This enables us to capture intricate details of facial appearance, clothing, and accessories, resulting in highly realistic avatar generations. Furthermore, we introduce pose-consistent constraint to the optimization process to enhance the multi-view consistency of synthesized head images from the diffusion model and thus eliminate interference from uncontrolled human poses. In addition, we present a multi-resolution rendering strategy that facilitates coarse-to-fine supervision of 3D avatar generation, thereby enhancing the performance of the proposed system. The resulting avatar model can be further edited using additional text descriptions and driven by motion sequences. Experiments show that AvatarBooth outperforms previous text-to-3D methods in terms of rendering and geometric quality from either text prompts or specific images. Please check our project website at https://zeng-yifei.github.io/avatarbooth_page/.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Multimodal Latent Diffusion Model for Complex Sewing Pattern Generation

    cs.CV 2024-12 conditional novelty 7.0 of 10

    SewingLDM generates complex, body-fitting sewing patterns from text, sketch, and body-shape conditions using a latent diffusion model with an extended pattern representation.

  2. Creating Your Editable 3D Photorealistic Avatar with Tetrahedron-constrained Gaussian Splatting

    cs.GR 2025-04 conditional novelty 6.0 of 10

    TetGS is a hybrid representation that embeds Gaussian kernels inside tetrahedral grids, enabling locally controlled geometric and appearance edits of 3D avatars reconstructed from monocular video.

  3. TeRA: Rethinking Text-guided Realistic 3D Avatar Generation

    cs.CV 2025-09 conditional novelty 5.0 of 10

    TeRA generates photorealistic 3D avatars from text in 12 seconds by training a latent diffusion model on a compact distilled latent space from a pretrained human reconstruction model.

  4. DevilSight: Augmenting Monocular Human Avatar Reconstruction through a Virtual Perspective

    cs.CV 2025-08 reject novelty 5.0 of 10

    A monocular human avatar reconstruction method generates pseudo back-view videos with a fine-tuned diffusion model and uses them as extra training data for a 3D Gaussian avatar.

  5. Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A hybrid of low-resolution backpropagation and high-resolution zeroth-order optimization, scheduled by a dynamic timestep-dependent probability, matches full-resolution fine-tuning quality while cutting training memory.

  6. SVAD: From Single Image to 3D Avatar via Synthetic Data Generation with Video Diffusion and Data Augmentation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    SVAD creates an animatable 3D Gaussian avatar from a single image by synthesizing a pose-driven video with MusePose, refining it with face fusion and restoration, and training a 3DGS avatar on the refined synthetic frames.

  7. Text-driven 3D Human Generation via Contrastive Preference Optimization

    cs.CV 2025-02 conditional novelty 5.0 of 10

    Contrastive preference optimization, combining ImageReward and PickScore with static and LLM-generated negative prompts, improves semantic alignment in SDS-based 3D human generation, especially for long prompts.

  8. SimAvatar: Simulation-Ready Avatars with Layered Hair and Clothing

    cs.CV 2024-12 conditional novelty 5.0 of 10

    SimAvatar generates text-described 3D avatars with separate body, garment, and hair layers that can be driven by off-the-shelf physics simulators.

Pith tools