Search Results for author: Junyoung Seo

Found 8 papers, 4 papers with code

Large Language Models are Frame-level Directors for Zero-shot Text-to-Video Generation

1 code implementation23 May 2023 Susung Hong, Junyoung Seo, Sunghwan Hong, Heeseong Shin, Seungryong Kim

In the paradigm of AI-generated content (AIGC), there has been increasing attention in extending pre-trained text-to-image (T2I) models to text-to-video (T2V) generation.

Text-to-Video Generation Video Generation +1

Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation

1 code implementation14 Mar 2023 Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Jaehoon Ko, Hyeonsu Kim, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, Seungryong Kim

Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting.

Single-View 3D Reconstruction Text to 3D

DiffFace: Diffusion-based Face Swapping with Facial Guidance

no code implementations27 Dec 2022 Kihong Kim, Yunho Kim, Seokju Cho, Junyoung Seo, Jisu Nam, Kychul Lee, Seungryong Kim, Kwanghee Lee

In this paper, we propose a diffusion-based face swapping framework for the first time, called DiffFace, composed of training ID conditional DDPM, sampling with facial guidance, and a target-preserving blending.

Face Swapping

MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation

1 code implementation22 Sep 2022 Junyoung Seo, Gyuseong Lee, Seokju Cho, Jiyoung Lee, Seungryong Kim

Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image.

Denoising Translation

ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization

1 code implementation18 Aug 2022 Jiwon Kim, Youngjo Min, Daehwan Kim, Gyuseong Lee, Junyoung Seo, Kwangrok Ryoo, Seungryong Kim

We present a novel semi-supervised learning framework that intelligently leverages the consistency regularization between the model's predictions from two strongly-augmented views of an image, weighted by a confidence of pseudo-label, dubbed ConMatch.

Pseudo Label

Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

no code implementations CVPR 2022 Jiwon Kim, Kwangrok Ryoo, Junyoung Seo, Gyuseong Lee, Daehwan Kim, Hansang Cho, Seungryong Kim

In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels, called SemiMatch.

Data Augmentation Semantic correspondence +1

AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning

no code implementations25 Jan 2022 Jiwon Kim, Kwangrok Ryoo, Gyuseong Lee, Seokju Cho, Junyoung Seo, Daehwan Kim, Hansang Cho, Seungryong Kim

In this paper, we address this limitation with a novel SSL framework for aggregating pseudo labels, called AggMatch, which refines initial pseudo labels by using different confident instances.

Pseudo Label

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