MvDeDiffusion: Multi-view Consistent Generation via Cross-view Deformable Attention for Denoising Diffusion Models
Abstract
Abstract Denoising diffusion models have demonstrated remarkable success in image generation, with numerous approaches achieving state-of-the-art synthesis quality. For autonomous driving applications, there is a critical need to extend these capabilities to multi-view image generation. However, achieving precise multi-view-consistent generation with 3D geometric awareness, critical for 3D perception tasks, remains challenging. Current approaches predominantly rely on overhead layout guidance, yet they frequently fail to maintain cross-view geometric coherence. This limitation manifests as misaligned object structures, discontinuous occlusions, and inconsistent depth relationships when synthesizing scenes from multiple angles. In this paper, we propose MvDeDiffusion, a diffusion-based framework for 3D-consistent multi-view image synthesis, which introduces two key innovations: (1) a cross-view deformable attention mechanism that explicitly enforces geometric and appearance consistency between adjacent viewpoints by adaptively aligning features domain in the denoising process, (2) a 3D-aware conditioning pipeline that integrates camera poses, foreground positional information, adjacent-view overlap to enable fine-grained control over scene structure while preserving photorealistic details. Our framework ensures view-consistent generation through explicit modeling of inter-perspective correlations during the diffusion process, overcoming the inherent limitations of independent…
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.