8 citations · 8 across the 13 of their papers we have counts for
12 papers · 1 filter
GUSH3R: Everyone Everywhere All at Once as Gaussians
Keito Abe, Kaede Shiohara, Takashi Otonari +1
Reconstructing dynamic human-scene environments from monocular videos is a challenging problem that requires jointly modeling scene geometry, camera motion, and non-rigid human dyn…
Wild3R: Feed-Forward 3D Gaussian Splatting from Unconstrained Sparse Photo Collection
Yuto Furutani, Takashi Otonari, Kaede Shiohara +1
Feed-forward 3D Gaussian Splatting (3DGS) removes the need for time-consuming per-scene optimization required by traditional 3DGS. However, existing feed-forward approaches struggl…
Unified Vector Floorplan Generation via Markup Representation
Kaede Shiohara, Toshihiko Yamasaki
Automatic residential floorplan generation has long been a central challenge bridging architecture and computer graphics, aiming to make spatial design more efficient and accessibl…
BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment
Risa Shinoda, Kaede Shiohara, Nakamasa Inoue +3
Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, h…
ExposeAnyone: Personalized Audio-to-Expression Diffusion Models Are Robust Zero-Shot Face Forgery Detectors
Kaede Shiohara, Toshihiko Yamasaki, Vladislav Golyanik
Detecting unknown deepfake manipulations remains one of the most challenging problems in face forgery detection. Current state-of-the-art approaches fail to generalize to unseen ma…
ControlVP: Interactive Geometric Refinement of AI-Generated Images with Consistent Vanishing Points
Ryota Okumura, Kaede Shiohara, Toshihiko Yamasaki
Recent text-to-image models, such as Stable Diffusion, have achieved impressive visual quality, yet they often suffer from geometric inconsistencies that undermine the structural r…
Robust Deepfake Detection for Electronic Know Your Customer Systems Using Registered Images
Takuma Amada, Kazuya Kakizaki, Taiki Miyagawa +3
In this paper, we present a deepfake detection algorithm specifically designed for electronic Know Your Customer (eKYC) systems. To ensure the reliability of eKYC systems against d…
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
Risa Shinoda, Kaede Shiohara
Automated animal face identification plays a crucial role in the monitoring of behaviors, conducting of surveys, and finding of lost animals. Despite the advancements in human face…
OpenAnimalTracks: A Dataset for Animal Track Recognition
Risa Shinoda, Kaede Shiohara
Animal habitat surveys play a critical role in preserving the biodiversity of the land. One of the effective ways to gain insights into animal habitats involves identifying animal…
Face2Diffusion for Fast and Editable Face Personalization
Kaede Shiohara, Toshihiko Yamasaki
Face personalization aims to insert specific faces, taken from images, into pretrained text-to-image diffusion models. However, it is still challenging for previous methods to pres…
BlendFace: Re-designing Identity Encoders for Face-Swapping
Kaede Shiohara, Xingchao Yang, Takafumi Taketomi
The great advancements of generative adversarial networks and face recognition models in computer vision have made it possible to swap identities on images from single sources. Alt…
Detecting Deepfakes with Self-Blended Images
Kaede Shiohara, Toshihiko Yamasaki
In this paper, we present novel synthetic training data called self-blended images (SBIs) to detect deepfakes. SBIs are generated by blending pseudo source and target images from s…