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3D Generation

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평가표

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보존된 평가 기록

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/leffff/FlowModels

Text-to-Image Rectified Flow as Plug-and-Play Priors

https://github.com/leffff/flow-matching-experiments

Text-to-Image Rectified Flow as Plug-and-Play Priors

https://github.com/liuff19/physics3d

Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion

https://github.com/lizuoyue/sat2scene

Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion

https://github.com/openrobotlab/pointllm

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/pointcept/sampart3d

SAMPart3D: Segment Any Part in 3D Objects

https://github.com/qizekun/ReCon

VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation

https://github.com/qizekun/ShapeLLM

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/ryanhangzhou/bootplace

Structured 3D Latents for Scalable and Versatile 3D Generation

https://github.com/urbanarchitect/urbanarchitect

Urban Architect: Steerable 3D Urban Scene Generation with Layout Prior

https://github.com/yuanzhi-zhu/prolific_dreamer2d

ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation

https://github.com/zhengxujosh/awesome-rag-vision

Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook

https://github.com/zrrskywalker/point-bind

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.