Xinting Hu (Postdoc)

We present SemanticNVS, a camera-conditioned multi-view diffusion model for novel view synthesis (NVS), which improves generation quality and consistency by integrating pre-trained semantic feature extractors. Existing NVS methods perform well for views near the input view, however, they tend to generate semantically implausible and distorted images under long-range camera motion, revealing severe degradation. We speculate that this degradation is due to current models failing to fully understand their conditioning or intermediate generated scene content. Here, we propose to integrate pre-trained semantic feature extractors to incorporate stronger scene semantics as conditioning to achieve high-quality generation even at distant viewpoints. We investigate two different strategies, (1) warped semantic features and (2) an alternating scheme of understanding and generation at each denoising step. Experimental results on multiple datasets demonstrate the clear qualitative and quantitative (4.69%-15.26% in FID) improvement over state-of-the-art alternatives.

Semantic segmentation requires dense pixel-level annotations, which are costly and time-consuming to acquire. To address this, we present SeSAM, a framework that uses a foundational segmentation model, i.e. Segment Anything Model (SAM), with weak labels, including coarse masks, scribbles, and points. SAM, originally designed for instance-based segmentation, cannot be directly used for semantic segmentation tasks. In this work, we identify specific challenges faced by SAM and determine appropriate components to adapt it for class-based segmentation using weak labels. Specifically, SeSAM decomposes class masks into connected components, samples point prompts along object skeletons, selects SAM masks using weak-label coverage, and iteratively refines labels using pseudo-labels, enabling SAM-generated masks to be effectively used for semantic segmentation. Integrated with a semi-supervised learning framework, SeSAM balances ground-truth labels, SAM-based pseudo-labels, and high-confidence pseudo-labels, significantly improving segmentation quality. Extensive experiments across multiple benchmarks and weak annotation types show that SeSAM consistently outperforms weakly supervised baselines while substantially reducing annotation cost relative to fine supervision.

We introduce PersonaHOI, a training- and tuning-free framework that fuses a
general StableDiffusion model with a personalized face diffusion (PFD) model to
generate identity-consistent human-object interaction (HOI) images. While
existing PFD models have advanced significantly, they often overemphasize
facial features at the expense of full-body coherence, PersonaHOI introduces an
additional StableDiffusion (SD) branch guided by HOI-oriented text inputs. By
incorporating cross-attention constraints in the PFD branch and spatial merging
at both latent and residual levels, PersonaHOI preserves personalized facial
details while ensuring interactive non-facial regions. Experiments, validated
by a novel interaction alignment metric, demonstrate the superior realism and
scalability of PersonaHOI, establishing a new standard for practical
personalized face with HOI generation. Our code will be available at
github.com/JoyHuYY1412/PersonaHOI

Recent advances in multi-modal generative models have enabled significant
progress in instruction-based image editing. However, while these models
produce visually plausible outputs, their capacity for knowledge-based
reasoning editing tasks remains under-explored. In this paper, we introduce
KRIS-Bench (Knowledge-based Reasoning in Image-editing Systems Benchmark), a
diagnostic benchmark designed to assess models through a cognitively informed
lens. Drawing from educational theory, KRIS-Bench categorizes editing tasks
across three foundational knowledge types: Factual, Conceptual, and Procedural.
Based on this taxonomy, we design 22 representative tasks spanning 7 reasoning
dimensions and release 1,267 high-quality annotated editing instances. To
support fine-grained evaluation, we propose a comprehensive protocol that
incorporates a novel Knowledge Plausibility metric, enhanced by knowledge hints
and calibrated through human studies. Empirical results on 10 state-of-the-art
models reveal significant gaps in reasoning performance, highlighting the need
for knowledge-centric benchmarks to advance the development of intelligent
image editing systems.

Large language models (LLMs) excel at natural language understanding and generation but remain vulnerable to factual errors, limiting their reliability in knowledge-intensive tasks. While decoding-time strategies provide a promising efficient solution without training, existing methods typically treat token-level and layer-level signals in isolation, overlooking the joint dynamics between them. In this work, we introduce a token-aware, layer-localized contrastive decoding method that aligns specific token types with their most influential transformer layers to improve factual generation. Through empirical attention analysis, we identify two key patterns: punctuation tokens receive dominant attention in early layers, while conceptual tokens govern semantic reasoning in intermediate layers. By selectively suppressing attention to these token types at their respective depths, we achieve the induction of controlled factual degradation and derive contrastive signals to guide the final factual decoding. Our method requires no additional training or model modification, and experiments demonstrate that our method consistently improves factuality across multiple LLMs and various benchmarks.