Summary Text-guided Reconstruction of Lifelike Clothed Humans arxiv.org
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One Line
The use of a personalized T2I diffusion model and VQA allows for the reconstruction of realistic 3D clothed humans from just one image.
Slides
Slide Presentation (9 slides)
Key Points
- TeCH is a method for reconstructing lifelike 3D clothed humans from a single image.
- It uses a personalized Text-to-Image (T2I) diffusion model and textual information derived from visual questioning answering (VQA).
- TeCH is related to image-based human reconstructors and 3D human generators.
- Various methods, such as CAPE, Chupa, gDNA, NPMs, and SPAMs, learn clothing details from 3D data.
- The proposed method, TeCH, outperforms other baselines in terms of both 3D metrics and 2D image quality metrics.
- DreamBooth is a model that personalizes a pre-trained diffusion model for subject-driven image generation.
- The authors propose a method that enhances facial details by sampling additional virtual cameras positioned around the face.
Summaries
19 word summary
TeCH uses a personalized T2I diffusion model and VQA to reconstruct lifelike 3D clothed humans from a single image.
32 word summary
TeCH is a method for reconstructing lifelike 3D clothed humans from a single image. It uses a personalized Text-to-Image (T2I) diffusion model and visual questioning answering (VQA) to guide the generation process
621 word summary
TeCH is a method for reconstructing lifelike 3D clothed humans from a single image. It uses a personalized Text-to-Image (T2I) diffusion model and textual information derived from visual questioning answering (VQA) to
TeCH is related to image-based human reconstructors and 3D human generators. The human reconstructors can be categorized into explicit-shape-based, implicit-function-based, and NeRF-based methods. Explicit-shape-based methods use parametric body models
Generative modeling of 3D clothed humans is achieved through statistical body models trained on 3D data. Various methods, such as CAPE, Chupa, gDNA, NPMs, and SPAMs, learn clothing details from
The document discusses the use of a personalized Text-to-Image diffusion model called DreamBooth to guide the generation process of lifelike clothed humans. It also introduces the Score Distillation Sampling (SDS) loss, which is used to optimize
The text excerpt describes a method for reconstructing lifelike clothed humans using a text-guided approach. The geometry network predicts the SDF value for each vertex, which is used to extract triangular meshes. The generated mesh is rendered using differentiable
The document discusses a method for reconstructing lifelike clothed humans using text guidance. The method consists of two stages: geometry and texture. In the geometry stage, a pixel-wise L2 loss and an edge distance loss are used to optimize the
TeCH, a text-guided reconstruction method, outperforms other baselines in terms of both 3D metrics and 2D image quality metrics. It accurately reconstructs clothed human geometry with intricate details and produces high-quality textures. The
TeCH is a text-guided reconstruction method for creating lifelike clothed humans. The study compares different influences on the reconstruction, including geometry and texture. The results show that TeCH is able to accurately recover the human shape and generate high-quality
The proposed method, TeCH, aims to reconstruct a lifelike 3D clothed human from a single image, leveraging descriptive text prompts and personalized Text-to-Image diffusion models. The method optimizes the 3D avatar, including parts
DreamBooth is a model that personalizes a pre-trained diffusion model for subject-driven image generation. It uses few-shot tuning and takes an initial noise and a text embedding to produce an image. DreamBooth fine-tunes the diffusion model using MSE
In the text-guided reconstruction of lifelike clothed humans, the authors propose a method that enhances facial details by sampling additional virtual cameras positioned around the face. They set the sampling parameters empirically and optimize the texture stage using various steps and iterations
Researchers have been exploring the use of text-guided reconstruction to generate lifelike clothed human avatars. Several studies have focused on different aspects of this technology. One study, conducted by Yee K Wong, developed a system called DreamAvatar that
Several papers and preprints related to the reconstruction and generation of lifelike clothed humans were referenced in this excerpt. These papers cover a range of topics including animatable avatars, clothed human reconstruction, neural radiance fields, view synthesis,
The text excerpt includes references to various research papers related to the reconstruction of lifelike clothed humans. The papers mentioned cover topics such as deep human parsing, high-fidelity clothed avatar reconstruction, text-guided human image generation, and human shape
This document is a list of references to various papers and studies related to the topic of text-guided reconstruction of lifelike clothed humans. The references include papers on deep face recognition, avatars in geography, expressive body capture, clothing capture and
This document contains a list of references to various papers and conferences related to the field of computer vision and 3D human reconstruction. The references include papers on topics such as 3D human reconstruction from a single image, text-guided image generation,
Several research papers related to the reconstruction of lifelike clothed humans were cited in this document. The papers cover various topics such as explicit clothed human optimization, lifting 2D photos to 3D objects, generative 3D human