activity
20242026
most citedSeed1.5-VL Technical Report

1 citations · 1 across the 6 of their papers we have counts for

collaborators
Showing cs.CVShow all

9 papers · 1 filter

cs.CV2026

Scaling Properties of Text Conditioning in Visual Generation

Zilong Chen, Chaorui Deng, Kunchang Li +2

We study empirical scaling properties for text conditioning in visual generation. Such properties have rarely been measured because diffusion loss does not scale with the number of…

cs.CV2026

MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation

Dewei Zhou, Xinyu Huang, Xun Wang +6

Generative visual models fundamentally struggle with precise spatial control. This arises from a core disconnect: models can process textual descriptions of space but cannot direct…

cs.CV2026

Context Unrolling in Omni Models

Ceyuan Yang, Zhijie Lin, Yang Zhao +16

We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such train…

cs.CV2026

UniGRPO: Unified Policy Optimization for Reasoning-Driven Visual Generation

Jie Liu, Zilyu Ye, Linxiao Yuan +8

Unified models capable of interleaved generation have emerged as a promising paradigm, with the community increasingly converging on autoregressive modeling for text and flow match…

cs.CV2025

VQ-VA World: Towards High-Quality Visual Question-Visual Answering

Chenhui Gou, Zilong Chen, Zeyu Wang +10

This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question -- an ability that has recently emerged in prop…

cs.CV2025

LightFusion: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation

Zeyu Wang, Zilong Chen, Chenhui Gou +8

Unified multimodal models have recently shown remarkable gains in both capability and versatility, yet most leading systems are still trained from scratch and require substantial c…