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[평범한 학부생이 하는 논문 리뷰] Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations (ICLR 2025) Project Page : https://rf-inversion.github.io/ Litu Rout1,2 Yujia Chen2 Nataniel Ruiz2 Constantine Caramanis1 Sanjay Shakkottai1Wen-Sheng Chu2 1 The University of Texas at Austin, 2 Google ICLR 202" data-og-host="rf-inversion.github.io" data-og-source-url="https://rf-inversion.github.io/" data-og-url="https://rf-inversion.github.io/" data-og-image=""> RF-InversionSemantic Image Inversion and .. 공감수 1 댓글수 0 2025. 2. 18.
[평범한 학부생이 하는 논문 리뷰] InstaFlow : One Step is Enough for High-Quality Diffusion-based Text-to-Image Generation (ICLR 2024) Paper : https://arxiv.org/abs/2309.06380 InstaFlow: One Step is Enough for High-Quality Diffusion-Based Text-to-Image GenerationDiffusion models have revolutionized text-to-image generation with its exceptional quality and creativity. However, its multi-step sampling process is known to be slow, often requiring tens of inference steps to obtain satisfactory results. Previous attemparxiv.orgAbstr.. 공감수 0 댓글수 1 2025. 2. 11.
[평범한 학부생이 하는 논문 리뷰] Flow Matching For Generative Modeling (ICLR 2023) Paper : https://arxiv.org/abs/2210.02747 Flow Matching for Generative ModelingWe introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs basearxiv.org(성민혁 교수님 강의 자료 참고 : https://www.youtube.com/watch?v=B4F.. 공감수 0 댓글수 0 2025. 2. 7.
[평범한 학부생이 하는 논문 리뷰] MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing (ICCV 2023) Paper : https://arxiv.org/abs/2304.08465 MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and EditingDespite the success in large-scale text-to-image generation and text-conditioned image editing, existing methods still struggle to produce consistent generation and editing results. For example, generation approaches usually fail to synthesize multiple imaarxiv.o.. 공감수 0 댓글수 0 2025. 1. 10.
[평범한 학부생이 하는 논문 리뷰] Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of Code (ICLR 2024) Paper : https://arxiv.org/abs/2310.01506 Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of CodeText-guided diffusion models have revolutionized image generation and editing, offering exceptional realism and diversity. Specifically, in the context of diffusion-based editing, where a source image is edited according to a target prompt, the process comarxiv.orgProject Page : https:.. 공감수 0 댓글수 0 2025. 1. 8.
[평범한 학부생이 하는 논문 리뷰] EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion Models (ICLR 2024) Paper : https://arxiv.org/abs/2401.11739 EmerDiff: Emerging Pixel-level Semantic Knowledge in Diffusion ModelsDiffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks. However, generating fine-grained segmentation masks with diffusion models often requires additional training on anarxiv.org0. Abstract Diffusion m.. 공감수 0 댓글수 0 2025. 1. 6.
[평범한 학부생이 하는 논문 리뷰] Video-P2P: Video Editing with Cross-attention Control (CVPR 2024) Paper : https://arxiv.org/abs/2303.04761 Video-P2P: Video Editing with Cross-attention ControlThis paper presents Video-P2P, a novel framework for real-world video editing with cross-attention control. While attention control has proven effective for image editing with pre-trained image generation models, there are currently no large-scale video gearxiv.orgGithub : https://github.com/dvlab-resea.. 공감수 0 댓글수 0 2025. 1. 3.
[평범한 학부생이 하는 논문 리뷰] WAVE: Warping DDIM Inversion Features for Zero-shot Text-to-Video Editing (ECCV 2024) 0. AbstractKey Challenge : Naive DDIM inversion process의 각 step에서의 randomness와 inaccuracy에 의해 발생하는 error를 제한하는 것.이는 video editing에서 temporal inconsistency를 야기할 수 있다.1. Introduction 본 논문은 diffusion model을 이용해서 zero-shot video editing method를 만드는 것을 목표로 한다. Inversion process는 temporally cohorent initial latents의 sequence를 제공함으로써 video editing 결과에 도움을 준다. 그러나 아래의 이미지처럼 direct inversion process는 pot.. 공감수 1 댓글수 0 2024. 12. 19.
[평범한 학부생이 하는 논문 리뷰] DreamMotion : Space-Time Self-Similar Score Distillation for Zero-shot Video Editing (ECCV 2024) Paper : https://arxiv.org/abs/2403.12002 DreamMotion: Space-Time Self-Similar Score Distillation for Zero-Shot Video EditingText-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion. Unlike existing video editing approaches, here we focus on score distillation sampling to circumvent the stanarxiv.orgProject P.. 공감수 0 댓글수 0 2024. 12. 14.
[평범한 학부생이 하는 논문 리뷰] ControlNeXt: Powerful and Efficient Control for Image and Video Generation (arXiv 2408) Paper : https://arxiv.org/abs/2408.06070 ControlNeXt: Powerful and Efficient Control for Image and Video GenerationDiffusion models have demonstrated remarkable and robust abilities in both image and video generation. To achieve greater control over generated results, researchers introduce additional architectures, such as ControlNet, Adapters and ReferenceNet, to intearxiv.orgGithub : https://g.. 공감수 0 댓글수 0 2024. 11. 29.
[평범한 학부생이 하는 논문 리뷰] DragAnything : Motion Control for Anything using Entity Representation (ECCV 2024) Paper : https://arxiv.org/abs/2403.07420 DragAnything: Motion Control for Anything using Entity RepresentationWe introduce DragAnything, which utilizes a entity representation to achieve motion control for any object in controllable video generation. Comparison to existing motion control methods, DragAnything offers several advantages. Firstly, trajectory-based isarxiv.orgProject Page : https://.. 공감수 1 댓글수 0 2024. 11. 12.
[평범한 학부생이 하는 논문 리뷰] MagDiff : Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing (ECCV 2024) Paper : https://arxiv.org/abs/2311.17338 MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and EditingThe diffusion model is widely leveraged for either video generation or video editing. As each field has its task-specific problems, it is difficult to merely develop a single diffusion for completing both tasks simultaneously. Video diffusion sorely relyinarxiv.org1. Introduc.. 공감수 8 댓글수 4 2024. 10. 31.
[평범한 학부생이 하는 논문 리뷰] VIDEOSHOP : Localized Semantic Video Editing with Noise-Extrapolated Diffusion Inversion (ECCV 2024) Paper : https://arxiv.org/abs/2403.14617v3 Videoshop: Localized Semantic Video Editing with Noise-Extrapolated Diffusion InversionWe introduce Videoshop, a training-free video editing algorithm for localized semantic edits. Videoshop allows users to use any editing software, including Photoshop and generative inpainting, to modify the first frame; it automatically propagates those charxiv.org0. .. 공감수 7 댓글수 3 2024. 10. 29.
[평범한 학부생이 하는 논문 리뷰] Style Aligned Image Generation via Shared Attention (CVPR 2024) Project Page : https://style-aligned-gen.github.io/ StyleAlignStyle Aligned Image Generation via Shared Attention CVPR 2024, Oral Amir Hertz* 1 Andrey Voynov* 1 Shlomi Fruchter† 1 Daniel Cohen-Or† 1,2 1 Google Research 2 Tel Aviv University *Indicates Equal Contribution †Indicates Equal Advising [Paper] style-aligned-gen.github.ioPaper : https://arxiv.org/abs/2312.02133 Style Aligned Image Ge.. 공감수 3 댓글수 2 2024. 10. 16.
[평범한 학부생이 하는 논문 리뷰] An Editing Friendly DDPM Noise Space : Inversion and Manipulations (CVPR 2024) Paper, Project Page, Github GitHub - inbarhub/DDPM_inversion: Official pytorch implementation of the paper: "An Edit Friendly DDPM Noise Space: Inversion anOfficial pytorch implementation of the paper: "An Edit Friendly DDPM Noise Space: Inversion and Manipulations". CVPR 2024. - GitHub - inbarhub/DDPM_inversion: Official pytorch implementa...github.com해결하려는 문제 본 논문에서는 기존 DDIM latent가 아닌 DDPM la.. 공감수 0 댓글수 1 2024. 9. 18.
[평범한 학부생이 하는 논문 리뷰] Null-text Inversion for Editing Real Images using Guided Diffusion Models (CVPR 2023) Paper | Github | Project Page Null-text Inversion for Editing Real Images using Guided Diffusion ModelsNull-text Inversion for Editing Real Images using Guided Diffusion Models Ron Mokady* 1,2 Amir Hertz* 1,2 Kfir Aberman1 Yael Pritch1 Daniel Cohen-Or1,2 1 Google Research 2 Tel Aviv University *Denotes Equal Contribution Paper Code TL;DR Null-textnull-text-inversion.github.io1. Introduction.. 공감수 1 댓글수 0 2024. 8. 28.
2달간의 AMI Lab Intern + 논문 작성 후기 8월 20일 화요일. 2달간의 랩실 인턴이 끝났다. 원래 인턴 시작하면서 세운 목표는 매주 한주간 뭘 했는지, 뭘 배웠는지, 어땠는지 등을 포스팅하려했지만 정말 정말 정신없고 힘들어서 그럴 여력은 없었다.... 이렇게 끝났을 떄 나마 후기를 작성하려고 한다.1달 간의 포항 생활 일단 POSTECH AMI Lab 인턴 생활이 어땠는지부터 정리해보자면 첫 한달은 포항에서 기숙사 생활을 했다. 대부분의 대학원생들이 포항계셔서 랩실은 나름 붐볐다. 랩실 시설은 엄청 깔끔하고 좋았다. 5층에 랩실이 있었는데 4층에 쉬는 공간과 회의실?이 따로 또 있었다. 적응이 끝나고 쉬는 공간에 있는 턱걸이 기구를 종종 이용했다. 첫 주는 연구 주제 관련된 3d editing 논문들을 쭉 읽고 서버 셋팅하는데 시간을 다 쓴 것.. 공감수 4 댓글수 0 2024. 8. 22.
[평범한 학부생이 하는 논문 리뷰] Prompt-to-Prompt Image Editing with Cross-Attention Control (ICLR 2023) Paper | Github | Project Page Prompt-to-PromptPrompt-to-Prompt Image Editing with Cross-Attention Control Amir Hertz1,2 Ron Mokady1,2 Jay Tenenbaum1 Kfir Aberman1 Yael Pritch1 Daniel Cohen-Or1,2 1 Google Research 2 Tel Aviv University Paper Code Abstract Recent large-scale text-driven synprompt-to-prompt.github.io1. Introduction 기존의 large-scale language-image (LLI) 모델들은 image editing 능력이 .. 공감수 0 댓글수 0 2024. 8. 12.
[평범한 학부생이 하는 논문 리뷰] Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation (CVPR 2023) Paper, Github, Project Page Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation"a photo of a pink toy horse on the beach" "a photo of a bronze horse in a museum" "a photo of a robot horse" "a photo of robots dancing" "a cartoon of a couple dancing" "a photo of a wooden sculpture of a couple dancing" "a photorealistic image of bear cupnp-diffusion.github.io1. Introduction .. 공감수 0 댓글수 0 2024. 7. 19.
[평범한 학부생이 하는 논문 리뷰] A Latent Space of Stochastic Diffusion Models for Zero-Shot Image Editing and Guidance (ICCV 2023) 논문 링크 : https://openaccess.thecvf.com/content/ICCV2023/papers/Wu_A_Latent_Space_of_Stochastic_Diffusion_Models_for_Zero-Shot_Image_ICCV_2023_paper.pdf깃헙 : https://github.com/ChenWu98/cycle-diffusion GitHub - ChenWu98/cycle-diffusion: [ICCV 2023] A latent space for stochastic diffusion models[ICCV 2023] A latent space for stochastic diffusion models - ChenWu98/cycle-diffusiongithub.com1. Introduc.. 공감수 1 댓글수 0 2024. 7. 18.
[평범한 학부생이 하는 논문 리뷰] TrAME : Trajectory-Anchored Multi-View Editing for Text-Guided 3D Gaussian Splatting Manipulation (arXiv 2407) https://arxiv.org/abs/2407.02034 TrAME: Trajectory-Anchored Multi-View Editing for Text-Guided 3D Gaussian Splatting ManipulationDespite significant strides in the field of 3D scene editing, current methods encounter substantial challenge, particularly in preserving 3D consistency in multi-view editing process. To tackle this challenge, we propose a progressive 3D editing strategy tarxiv.org1. I.. 공감수 2 댓글수 0 2024. 7. 13.
[평범한 학부생이 하는 논문 리뷰] SyncNoise : Geometrically Consistent Noise Prediction for Text-based 3D Scene Editing (arXiv 2406) https://arxiv.org/abs/2406.17396 SyncNoise: Geometrically Consistent Noise Prediction for Text-based 3D Scene EditingText-based 2D diffusion models have demonstrated impressive capabilities in image generation and editing. Meanwhile, the 2D diffusion models also exhibit substantial potentials for 3D editing tasks. However, how to achieve consistent edits across multiplearxiv.org1. Introduction I.. 공감수 2 댓글수 5 2024. 7. 6.
[평범한 학부생이 하는 논문 리뷰] Posterior Distillation Sampling (CVPR 2024) https://posterior-distillation-sampling.github.io/ Posterior Distillation SamplingWe introduce Posterior Distillation Sampling (PDS), a novel optimization method for parametric image editing based on diffusion models. Existing optimization-based methods, which leverage the powerful 2D prior of diffusion models to handle various parametrposterior-distillation-sampling.github.io1. Introduction Edi.. 공감수 1 댓글수 1 2024. 7. 2.
[평범한 학부생이 하는 논문 리뷰] DreamGaussian : Generative Gaussian Splatting for Efficient 3D Content Creation (ICLR 2024 oral) https://arxiv.org/abs/2309.16653 DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content CreationRecent advances in 3D content creation mostly leverage optimization-based 3D generation via score distillation sampling (SDS). Though promising results have been exhibited, these methods often suffer from slow per-sample optimization, limiting their practiarxiv.org1. Introduction 최근 3D .. 공감수 2 댓글수 1 2024. 7. 2.
[평범한 학부생이 하는 논문 리뷰] DreamFusion : Text-to-3D using 2D Diffusion (ICLR 2023) https://arxiv.org/abs/2209.14988 DreamFusion: Text-to-3D using 2D DiffusionRecent breakthroughs in text-to-image synthesis have been driven by diffusion models trained on billions of image-text pairs. Adapting this approach to 3D synthesis would require large-scale datasets of labeled 3D data and efficient architectures for denoiarxiv.org1. Introduction Diffusion model은 다양한 다른 modality에서 적용되는데 성.. 공감수 0 댓글수 0 2024. 6. 27.
네이버 부스트캠프 그 후 이야기 네이버 부스트캠프 수료를 하고 약 3개월이 되간다.... 부스트캠프를 시작하기 전에 내가 상상하던 모습은 아니긴하다ㅎㅎ 부스트캠프를 시작하기 전에 1년 휴학을 하고 세웠던 계획은 부스트캠프를 통해 엄청난 성장을 하고 복학 전까지 인턴을 하는 것이 목표였으나 역시 인생은 계획대로 되지 않는다. 부스트캠프를 시작하면서 한 다짐만큼 내가 열심히 하지 않은 탓이 아닐까.. 그래도 그 만큼의 성장은 아니였지만 당연히 5~6개월간 나름대로 성장은 했고 또 많은 경험을 했고 또 많은 사람들은 만나면서 정말 잊지 못할 경험을 한 것은 사실이다. 주변에 네이버 부스트캠프 ai tech를 하고 싶어하는 사람들이 좀 있는데 나는 정말 x100 강추한다. 아무튼 수료하면서 수료 후기? 포스팅을 할려고 했지만 나의 귀차니즘으로.. 공감수 3 댓글수 0 2024. 6. 20.
[평범한 학부생이 하는 논문 리뷰] Instruct-NeRF2NeRF : Editing 3D Scene with Instructions (ICCV 2023 oral) https://arxiv.org/abs/2303.12789 Instruct-NeRF2NeRF: Editing 3D Scenes with InstructionsWe propose a method for editing NeRF scenes with text-instructions. Given a NeRF of a scene and the collection of images used to reconstruct it, our method uses an image-conditioned diffusion model (InstructPix2Pix) to iteratively edit the input images whiarxiv.org1. Introduction NeRF와 같은 3D reconstruction 기술.. 공감수 0 댓글수 0 2024. 6. 19.
[평범한 학부생이 하는 논문 리뷰] Text2Room : Extracting Textured 3D Meshes from 2D Text-to-Image Models (ICCV 2023 oral) https://arxiv.org/abs/2303.11989 Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image ModelsWe present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input. To this end, we leverage pre-trained 2D text-to-image models to synthesize a sequence of images from different poses. In order to lift these outparxiv.org요약다루는 task : 2D Text-to-Image m.. 공감수 1 댓글수 1 2024. 6. 16.
[평범한 학부생이 하는 논문 리뷰] 3D Gaussian Splatting for Real-Time Radiance Field Rendering (SIGGRAPH 2023) https://arxiv.org/abs/2308.04079 3D Gaussian Splatting for Real-Time Radiance Field RenderingRadiance Field methods have recently revolutionized novel-view synthesis of scenes captured with multiple photos or videos. However, achieving high visual quality still requires neural networks that are costly to train and render, while recent faster methoarxiv.org1. Introduction NeRF 기반의 방식들은 high quali.. 공감수 0 댓글수 2 2024. 6. 10.
[평범한 학부생이 하는 논문 리뷰] NeRF : Representing Scenes as Neural Radiance Fields for View Synthesis (ECCV 2020 oral) https://arxiv.org/abs/2003.08934 NeRF: Representing Scenes as Neural Radiance Fields for View SynthesisWe present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-conarxiv.org 랩실 인턴을 시작하기 전에 연구 주제에 대한 레퍼런스.. 공감수 0 댓글수 1 2024. 6. 5.

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