4 citations · 4 across the 1 of their papers we have counts for
6 papers · 1 filter
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale
Baorui Ma, Huachen Gao, Haoge Deng +4
Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrai…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…
Generative Multimodal Models are In-Context Learners
Quan Sun, Yufeng Cui, Xiaosong Zhang +8
The human ability to easily solve multimodal tasks in context (i.e., with only a few demonstrations or simple instructions), is what current multimodal systems have largely struggl…
GeoDream: Disentangling 2D and Geometric Priors for High-Fidelity and Consistent 3D Generation
Baorui Ma, Haoge Deng, Junsheng Zhou +3
Text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models has shown great promise but still suffers from inconsistent 3D geometric structures (Janus…
Uni3D: Exploring Unified 3D Representation at Scale
Junsheng Zhou, Jinsheng Wang, Baorui Ma +3
Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable…