IP-ConTex: detail-consistent texture generation with image prompt
Abstract
Textures are critical for enhancing the visual fidelity and diversity of three-dimensional (3D) models. Recently, generative models have significantly advanced texture generation. However, fine-grained control of the generation process remains challenging. Hence, we propose IP-ConTex, which is a novel image-guided texture-generation method that introduces appearance control into the diffusion process to ensure detail-consistent results. First, synchronized multiview diffusion is employed to maintain structural and layout consistency across multiple views. Subsequently, a new appearance-control module is designed for the pretrained diffusion model. By leveraging cross-attention control, self-attention control, diffusion feature synchronization, and color adjustment, appearance information from the reference image is effectively extracted and injected into the texture-generation process. Experimental results demonstrate that IP-ConTex successfully transfers appearance details to 3D geometries without fine-tuning or optimization, thus achieving high-quality and detail-consistent texture generation.
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.