9 papers
VIF-Bench: Evaluating Visual Instruction Following in Multi-Reference Image Generation
Yuta Oshima, Masakazu Yoshimura, Masahiro Suzuki +2
Recent multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not only by text but also by visual…
AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation
Yuta Oshima, Ku Onoda, Yusuke Iwasawa +3
Recent image generation models can take multiple reference images as input and combine them into a new image. However, multi-reference image generation remains challenging: models…
Multi-Axis Max@K Reinforcement Learning for Representative Diversity in Text-to-Image Generation
Ku Onoda, Paavo Parmas, Hiroki Furuta +4
Text-to-image (T2I) models can synthesize realistic, prompt-aligned images, yet samples generated for the same prompt often cover only a small subset of visually distinct modes. Th…
WorldPack: Dynamic Frame Compression for Long-context Video World Modeling
Yuta Oshima, Yusuke Iwasawa, Masahiro Suzuki +2
Video world models have attracted significant attention for their ability to produce high-fidelity future visual observations conditioned on past observations and navigation action…
MultiBanana: A Challenging Benchmark for Multi-Reference Text-to-Image Generation
Yuta Oshima, Daiki Miyake, Kohsei Matsutani +4
Recent text-to-image generation models have acquired the ability of multi-reference generation and editing; that is, to inherit the appearance of subjects from multiple reference i…
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search
Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo +1
The remarkable progress in text-to-video diffusion models enables the generation of photorealistic videos, although the content of these generated videos often includes unnatural m…
ADOPT: Modified Adam Can Converge with Any with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi +7
Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., ,…
Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference
Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo
Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accura…
SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
Yuta Oshima, Shohei Taniguchi, Masahiro Suzuki +1
Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation.…