23 citations · 80 across the 22 of their papers we have counts for
19 papers · 1 filter
MVTrack4Gen: Multi-View Point Tracking as Geometric Supervision for 4D Video Generation
JoungBin Lee, Jaewoo Jung, Jongmin Lee +8
Synthesizing a novel-view video from a monocular reference video along a target camera trajectory requires both geometric consistency and motion fidelity with respect to the refere…
4D Reconstruction from Sparse Dynamic Cameras
Kazuki Ozeki, Shun Kenney, Yuto Shibata +6
Although dynamic 3D (i.e., 4D) reconstruction from a monocular dynamic camera has recently advanced, it remains fundamentally limited by depth ambiguity. In this paper, we focus on…
Learning Global Motion with Compact Gaussians for Feed-Forward 4D Reconstruction
Mungyeom Kim, Minkyeong Jeon, Honggyu An +10
Dynamic scene reconstruction from monocular video remains a fundamental challenge in computer vision. Existing feed-forward methods predict 3D Gaussians pixel-wise for each frame,…
C3G: Learning Compact 3D Representations with 2K Gaussians
Honggyu An, Jaewoo Jung, Mungyeom Kim +10
Reconstructing and understanding 3D scenes from unposed sparse views in a feed-forward manner remains as a challenging task in 3D computer vision. Recent approaches use per-pixel 3…
3D Scene Prompting for Scene-Consistent Camera-Controllable Video Generation
JoungBin Lee, Jaewoo Jung, Jisang Han +6
We present 3DScenePrompt, a framework that generates the next video chunk from arbitrary-length input while enabling precise camera control and preserving scene consistency. Unlike…
Vid-CamEdit: Video Camera Trajectory Editing with Generative Rendering from Estimated Geometry
Junyoung Seo, Jisang Han, Jaewoo Jung +9
We introduce Vid-CamEdit, a novel framework for video camera trajectory editing, enabling the re-synthesis of monocular videos along user-defined camera paths. This task is challen…
DUSt3R: Enhancing 3D Reconstruction for Dynamic Scenes
Jisang Han, Honggyu An, Jaewoo Jung +7
In this work, we address the task of 3D reconstruction in dynamic scenes, where object motions frequently degrade the quality of previous 3D pointmap regression methods, such as DU…
HumanGif: Single-View Human Diffusion with Generative Prior
Shoukang Hu, Takuya Narihira, Kazumi Fukuda +3
Previous 3D human creation methods have made significant progress in synthesizing view-consistent and temporally aligned results from sparse-view images or monocular videos. Howeve…
GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping
Junyoung Seo, Kazumi Fukuda, Takashi Shibuya +6
Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a mo…
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
Hiromichi Kamata, Yuiko Sakuma, Akio Hayakawa +2
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D. Our method is designed for a novel task, which is to convert a given 3D scene to another scene according to…
DetOFA: Efficient Training of Once-for-All Networks for Object Detection Using Path Filter
Yuiko Sakuma, Masato Ishii, Takuya Narihira
We address the challenge of training a large supernet for the object detection task, using a relatively small amount of training data. Specifically, we propose an efficient superne…
NDJIR: Neural Direct and Joint Inverse Rendering for Geometry, Lights, and Materials of Real Object
Kazuki Yoshiyama, Takuya Narihira
The goal of inverse rendering is to decompose geometry, lights, and materials given pose multi-view images. To achieve this goal, we propose neural direct and joint inverse renderi…
Fine-grained Image Editing by Pixel-wise Guidance Using Diffusion Models
Naoki Matsunaga, Masato Ishii, Akio Hayakawa +2
Our goal is to develop fine-grained real-image editing methods suitable for real-world applications. In this paper, we first summarize four requirements for these methods and propo…
Thinking the Fusion Strategy of Multi-reference Face Reenactment
Takuya Yashima, Takuya Narihira, Tamaki Kojima
In recent advances of deep generative models, face reenactment -manipulating and controlling human face, including their head movement-has drawn much attention for its wide range o…
Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision
Andrew Shin, Masato Ishii, Takuya Narihira
Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years. Its succes…
Reference-Based Video Colorization with Spatiotemporal Correspondence
Naofumi Akimoto, Akio Hayakawa, Andrew Shin +1
We propose a novel reference-based video colorization framework with spatiotemporal correspondence. Reference-based methods colorize grayscale frames referencing a user input color…
Direct Intrinsics: Learning Albedo-Shading Decomposition by Convolutional Regression
Takuya Narihira, Michael Maire, Stella X. Yu
We introduce a new approach to intrinsic image decomposition, the task of decomposing a single image into albedo and shading components. Our strategy, which we term direct intrinsi…
Affinity CNN: Learning Pixel-Centric Pairwise Relations for Figure/Ground Embedding
Michael Maire, Takuya Narihira, Stella X. Yu
Spectral embedding provides a framework for solving perceptual organization problems, including image segmentation and figure/ground organization. From an affinity matrix describin…
Mapping Images to Sentiment Adjective Noun Pairs with Factorized Neural Nets
Takuya Narihira, Damian Borth, Stella X. Yu +2
We consider the visual sentiment task of mapping an image to an adjective noun pair (ANP) such as "cute baby". To capture the two-factor structure of our ANP semantics as well as t…