3 citations · 10 across the 16 of their papers we have counts for
10 papers · 1 filter
Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes
Junlin Han, Shengbang Tong, David Fan +4
Vision offers a critical axis for advancing foundation models, driving a shift towards natively unified multimodal pretraining. Despite this momentum, the design space and the fund…
From Pixels to Feelings: Aligning MLLMs with Human Cognitive Perception of Images
Yiming Chen, Junlin Han, Tianyi Bai +3
While Multimodal Large Language Models (MLLMs) are adept at answering what is in an image-identifying objects and describing scenes-they often lack the ability to understand how an…
WildCAT3D: Appearance-Aware Multi-View Diffusion in the Wild
Morris Alper, David Novotny, Filippos Kokkinos +2
Despite recent advances in sparse novel view synthesis (NVS) applied to object-centric scenes, scene-level NVS remains a challenge. A central issue is the lack of available clean m…
Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects
Shalini Maiti, Lourdes Agapito, Filippos Kokkinos
Rapid advancements in text-to-3D generation require robust and scalable evaluation metrics that align closely with human judgment, a need unmet by current metrics such as PSNR and…
VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Models
Yufan Ren, Konstantinos Tertikas, Shalini Maiti +4
Large Vision-Language Models (LVLMs) struggle with puzzles, which require precise perception, rule comprehension, and logical reasoning. Assessing and enhancing their performance i…
Flex3D: Feed-Forward 3D Generation with Flexible Reconstruction Model and Input View Curation
Junlin Han, Jianyuan Wang, Andrea Vedaldi +2
Generating high-quality 3D content from text, single images, or sparse view images remains a challenging task with broad applications. Existing methods typically employ multi-view…
Meta 3D Gen
Raphael Bensadoun, Tom Monnier, Yanir Kleiman +17
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation. 3DGen offers 3D asset creation with high prompt fidelity and high-quality 3…
Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR Materials
Yawar Siddiqui, Tom Monnier, Filippos Kokkinos +8
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control. Compared…
VFusion3D: Learning Scalable 3D Generative Models from Video Diffusion Models
Junlin Han, Filippos Kokkinos, Philip Torr
This paper presents a novel method for building scalable 3D generative models utilizing pre-trained video diffusion models. The primary obstacle in developing foundation 3D generat…
IM-3D: Iterative Multiview Diffusion and Reconstruction for High-Quality 3D Generation
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht +4
Most text-to-3D generators build upon off-the-shelf text-to-image models trained on billions of images. They use variants of Score Distillation Sampling (SDS), which is slow, somew…