clip (5) 썸네일형 리스트형 [KO-stable-diffusion-anything] - 한국어 기반의 stable-diffusion-disney와 KO-anything-v4-5 Github: https://github.com/KyujinHan/KO-stable-diffusion-anything GitHub - KyujinHan/KO-stable-diffusion-anything: Diffusion-based korean text-to-image generation model Diffusion-based korean text-to-image generation model - GitHub - KyujinHan/KO-stable-diffusion-anything: Diffusion-based korean text-to-image generation model github.com KO-anything-v4-5: https://huggingface.co/kyujinpy/KO-anythi.. [DietNeRF 논문 리뷰] - Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis *DietNeRF를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! DietNeRF paper: [2104.00677] Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis (arxiv.org) Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis We present DietNeRF, a 3D neural scene representation estimated from a few images. Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene.. [CLIP-NeRF 논문 리뷰] - CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields *해당 글은 CLIP-NeRF 논문 리뷰를 위한 글입니다. 궁금하신 점은 댓글로 남겨주세요! CLIP-NeRF paper: [2112.05139] CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields (arxiv.org) CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields We present CLIP-NeRF, a multi-modal 3D object manipulation method for neural radiance fields (NeRF). By leveraging the joint language-image embedding space of t.. [NeRF-Art 논문 리뷰] - Text-Driven Neural Radiance Fields Stylization *NeRF-Art 논문 리뷰 글입니다! 궁금하신 점이 있다면 댓글로 남겨주세요! NeRF-Art paper: [2212.08070] NeRF-Art: Text-Driven Neural Radiance Fields Stylization (arxiv.org) NeRF-Art: Text-Driven Neural Radiance Fields Stylization As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially.. [CLIP 논문 리뷰] - Learning Transferable Visual Models From Natural Language Supervision *CLIP 논문 리뷰를 위한 글입니다. 질문이 있다면 댓글로 남겨주시길 바랍니다! CLIP paper: [2103.00020] Learning Transferable Visual Models From Natural Language Supervision (arxiv.org) Learning Transferable Visual Models From Natural Language Supervision State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and.. 이전 1 다음
