VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness

• URL: http://arxiv.org/abs/2503.16406v1
• Date: Thu, 20 Mar 2025 17:56:20 GMT
• Title: VerbDiff: Text-Only Diffusion Models with Enhanced Interaction Awareness
• Authors: SeungJu Cha, Kwanyoung Lee, Ye-Chan Kim, Hyunwoo Oh, Dong-Jin Kim,
• Abstract summary: VerbDiff is a novel text-to-image generation model that weakens the bias between interaction words and objects.<n>Our approach enables the model to accurately understand the intended interaction between humans and objects, producing high-quality images.
• Score: 5.542712070598464
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent large-scale text-to-image diffusion models generate photorealistic images but often struggle to accurately depict interactions between humans and objects due to their limited ability to differentiate various interaction words. In this work, we propose VerbDiff to address the challenge of capturing nuanced interactions within text-to-image diffusion models. VerbDiff is a novel text-to-image generation model that weakens the bias between interaction words and objects, enhancing the understanding of interactions. Specifically, we disentangle various interaction words from frequency-based anchor words and leverage localized interaction regions from generated images to help the model better capture semantics in distinctive words without extra conditions. Our approach enables the model to accurately understand the intended interaction between humans and objects, producing high-quality images with accurate interactions aligned with specified verbs. Extensive experiments on the HICO-DET dataset demonstrate the effectiveness of our method compared to previous approaches.

Related papers

• Generating Fine Details of Entity Interactions [17.130839907951877]
  This paper introduces InterActing, an interaction-focused dataset with 1000 fine-grained prompts covering three key scenarios. We propose a decomposition-augmented refinement procedure to address interaction generation challenges. Our approach, DetailScribe, uses a VLM to critique generated images, and applies targeted interventions within the diffusion process in refinement.
  arXiv  Detail & Related papers  (2025-04-11T17:24:58Z)
• Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models [65.82564074712836]
  We introduce DIFfusionHOI, a new HOI detector shedding light on text-to-image diffusion models. We first devise an inversion-based strategy to learn the expression of relation patterns between humans and objects in embedding space. These learned relation embeddings then serve as textual prompts, to steer diffusion models generate images that depict specific interactions.
  arXiv  Detail & Related papers  (2024-10-26T12:00:33Z)
• Distractors-Immune Representation Learning with Cross-modal Contrastive Regularization for Change Captioning [71.14084801851381]
  Change captioning aims to succinctly describe the semantic change between a pair of similar images. Most existing methods directly capture the difference between them, which risk obtaining error-prone difference features. We propose a distractors-immune representation learning network that correlates the corresponding channels of two image representations.
  arXiv  Detail & Related papers  (2024-07-16T13:00:33Z)
• Hire: Hybrid-modal Interaction with Multiple Relational Enhancements for Image-Text Matching [7.7559623054251]
  Image-text matching (ITM) is a fundamental problem in computer vision. We propose a Hybrid-modal feature the Interaction with multiple Enhancements (termed textitHire) for image-text matching. In particular, the explicit intra-modal spatial-semantic graph-based reasoning network is designed to improve the contextual representation of visual objects.
  arXiv  Detail & Related papers  (2024-06-05T13:10:55Z)
• THOR: Text to Human-Object Interaction Diffusion via Relation Intervention [51.02435289160616]
  We propose a novel Text-guided Human-Object Interaction diffusion model with Relation Intervention (THOR) In each diffusion step, we initiate text-guided human and object motion and then leverage human-object relations to intervene in object motion. We construct Text-BEHAVE, a Text2HOI dataset that seamlessly integrates textual descriptions with the currently largest publicly available 3D HOI dataset.
  arXiv  Detail & Related papers  (2024-03-17T13:17:25Z)
• Contextualized Diffusion Models for Text-Guided Image and Video Generation [67.69171154637172]
  Conditional diffusion models have exhibited superior performance in high-fidelity text-guided visual generation and editing. We propose a novel and general contextualized diffusion model (ContextDiff) by incorporating the cross-modal context encompassing interactions and alignments between text condition and visual sample. We generalize our model to both DDPMs and DDIMs with theoretical derivations, and demonstrate the effectiveness of our model in evaluations with two challenging tasks: text-to-image generation, and text-to-video editing.
  arXiv  Detail & Related papers  (2024-02-26T15:01:16Z)
• From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models [38.14123683674355]
  We propose a method to utilize the attention mechanism in the denoising network of text-to-image diffusion models. We evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under weakly-supervised semantic segmentation setting. Our work reveals a novel way to extract the rich multi-modal knowledge hidden in diffusion models for segmentation.
  arXiv  Detail & Related papers  (2023-09-08T04:10:01Z)
• Learning Human-Human Interactions in Images from Weak Textual Supervision [15.71242350678679]
  We propose a new paradigm of learning human-human interactions as free text from a single still image. We show that the pseudo-labels produced by this procedure can be used to train a captioning model. We will release our code and pseudo-labels along with Waldo and Wenda, a manually-curated test set for still image human-human interaction understanding.
  arXiv  Detail & Related papers  (2023-04-27T11:32:48Z)
• Learning to Model Multimodal Semantic Alignment for Story Visualization [58.16484259508973]
  Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. We explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model.
  arXiv  Detail & Related papers  (2022-11-14T11:41:44Z)
• FILIP: Fine-grained Interactive Language-Image Pre-Training [106.19474076935363]
  Fine-grained Interactive Language-Image Pre-training achieves finer-level alignment through a cross-modal late interaction mechanism. We construct a new large-scale image-text pair dataset called FILIP300M for pre-training. Experiments show that FILIP achieves state-of-the-art performance on multiple downstream vision-language tasks.
  arXiv  Detail & Related papers  (2021-11-09T17:15:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.