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版本更新 · v3arXiv:2608.20336

WithEveryone: Unified Planning and Identity Grounding for Group Image Generation

Hengyuan Xu, Qixun Wang, Yiji Cheng, Miles Yang, Zhao Zhong, Wei Cheng, Xingjun Ma

原文摘要

Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces. We introduce WithEveryone, a unified framework for generating group images of 5-10 reference identities. WithEveryone injects each selected identity as an addressed token, predicts a structured identity-layout plan, and renders the plan as a visual condition. Its key objective, Layout-Grounded ID Loss, uses annotated face regions to supervise the intended identities directly, avoiding unstable embedding-based face matching; ID Representation Forcing additionally trains a prediction for each identity before image synthesis. WithEveryone leads in identity similarity (0.538 versus GPT-Image-2.5's 0.472), with lower Copy-Paste (0.149 versus 0.197), 94.5% identity coverage, and 9.6% identity collisions. These results show that explicit identity-layout grounding enables identity-preserving generation to scale to larger groups without relying on direct reference-face copying.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04386

StepX-OPD: Rethinking Multi-Teacher On-Policy Distillation in Diffusion Models

Chenru Wang, Xuanyang Zhang, Yucheng Han, Wenbo Nie, Wei Cheng, Xianfang Zeng, Gang YU, Chi Zhang

原文摘要

A deployable text-to-image diffusion model must hold several capabilities at once, such as instruction following, text rendering and human preference, and must sample in a few steps. Capabilities and few-step sampling are built in separate stages: a full-step model first consolidates capabilities through multi-teacher on-policy distillation, then serves as a fixed teacher for step distillation. Every full-step model we evaluate scores lower once compressed, and the second stage cannot begin until the first finishes. We introduce StepX-OPD, which merges the two stages: one student acquires every capability while learning to sample in a few steps, so frozen specialists supervise it on the states its own rollout visits rather than on a longer trajectory it never runs. Because a decomposition of the rollout's KL weights velocity mismatch most heavily near the clean end, we also regress the student's velocity on the low-noise part of the rollout. Separately, we find that capability integration makes models write prompt words into images on prompts that never asked for text, and we give a variant that lowers it. We evaluate on SD3.5-Medium and FLUX.1-dev across instruction following, text rendering, image quality and human preference. On both architectures, StepX-OPD attains the highest normalized average score among the four-step methods compared and exceeds the forty-step Flow-OPD model, and on SD3.5-Medium it trains with about 37% fewer GPU-hours than sequential DiffusionOPD followed by step distillation.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39617

UniTele: Learning Device-Conditioned Degradations for Unified Smartphone Telephoto Super-Resolution

Yuchen Zhang, Aojie Li, Wei Cheng, Xianfang Zeng, Runxing Liu, haixin hao, Gang YU, Xiaoyun Yuan

原文摘要

Super-telephoto imaging on mobile phones suffers from complex, device-dependent degradations that are poorly captured by conventional handcrafted pipelines. Collecting large-scale aligned training pairs across different phones and diverse scenes, however, is both costly and difficult to scale. We therefore study whether device-specific degradations can be learned from only a small number of physical captures and transferred for training a unified super-resolution (SR) model. Our key idea is to factor degradation learning into two learnable components: one captures the device-specific imaging characteristics under well-resolved conditions, while the other captures the additional information loss and artifacts that arise in super-telephoto imaging. We build a paired benchmark across three phone brands (Huawei, Apple, and vivo) by photographing the same content at two display sizes and zoom settings. The larger displayed image is captured at the phone's maximum optical zoom setting, while the smaller image is photographed directly at the phone's highest native zoom setting through the phone's native imaging pipeline. The benchmark contains 519 training and 60 held-out pairs. We use the benchmark to learn device-specific degradation, then apply it to external images to synthesize thousands of additional pairs to train our UniTele SR model. Relative to the pretrained Qwen and FLUX backbones before adaptation, the full UniTele training recipe substantially improves paired restoration fidelity and perceptual similarity on the held-out benchmark and yields markedly more stable and visually faithful restorations.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36327

Training-Free Inference Acceleration for Video Large Language Models: Methods, Trade-offs, and Evaluation

Yuqi Li, Chuanyue Yan, Neil Rodrigues, Xianglong Wang, Xiaoqin Feng, Junming Liu, Xi Xiao, Zhongwei Cheng 等 11 位作者

原文摘要

Video Large Language Models (VLMs) have demonstrated strong capabilities in video understanding and reasoning, but processing long visual sequences incurs substantial computational, latency, and memory overhead. This survey examines training-free inference acceleration, focusing on reducing inference costs while preserving task-relevant spatiotemporal evidence. We organize existing methods by their intervention stages in the inference pipeline, covering input, visual encoding, projector, prefill, and decoding. Post-training quantization (PTQ) is discussed separately as a cross-stage extension with varying calibration requirements. We analyze how intervention position affects evidence preservation and computational savings, and why local efficiency improvements may not translate into end-to-end acceleration. We complement this analysis with quantitative evidence from prior studies and our own experiments under a shared evaluation setting to assess accuracy-latency trade-offs.

cs.CV

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1356ACL Anthology · ACL 2026

Zero-shot Jianzi Recognition as Structured Visual Information Extraction in Open Compositional Symbolic Systems

Zehan Li Northeastern University, Fu Zhang Northeastern University, Zhijun Liu, Jingwei Cheng Northeastern University, China

原文摘要

Guqin (古琴) Jianzi (減字) is an open and freely compositional tablature system that encodes performance actions rather than acoustic outcomes. Its automatic recognition remains largely unexplored, as conventional OCR assumes a closed and enumerable glyph set and struggles with Jianzi’s unbounded composition and manuscript-level variability.We introduce Zero-shot Jianzi Recognition, which formulates Jianzi recognition as vision-to-sequence prediction of canonical component sequences under a zero-shot split. To enable scalable supervision, we construct Synthetic-JZ from aligned online composition metadata. We then synthesize manuscript-like training images via component-wise style recomposition and manuscript-domain noise modeling, and fine-tune a vision–language model for end-to-end component sequence recognition. At inference time, a lightweight legality-guided correction module re-ranks decoding candidates, suppressing structural hallucinations without modifying the backbone.Experiments on two benchmarks show that our method achieves 63.02% sequence accuracy on Real-JZ, our manually annotated real-world Jianzi benchmark, surpassing Gemini-3-Pro by 35.11%. This result highlights the feasibility of reliable automated Jianzi recognition and its potential for large-scale digitization of historical Guqin Jianzi Pu manuscripts.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表