Reconstructing detailed geometry and realistic appearance from a single RGB image is essential yet fundamentally challenging due to inherent ambiguities such as occlusion, lighting variations, and texture-geometry entanglement. While recent diffusion-based generative models have significantly improved novel view synthesis, existing approaches suffer from two critical limitations: lack of cross-view geometric consistency and insufficient cross-domain semantic alignment. To address these issues, we introduce UniCross3D, a unified cross-view and cross-domain diffusion framework designed explicitly for consistent and physically coherent 3D generation. UniCross3D features two novel contributions: (1) a cross-view latent regularization that enforces cross-view geometric consistency across synthesized viewpoints by penalizing latent variance, and (2) a cross-domain mutual information objective grounded in the physics of image formation, explicitly aligning synthesized color and normal maps. Extensive experiments demonstrate that UniCross3D achieves significantly improved view consistency and semantic alignment over state-of-the-art methods and yields higher-fidelity reconstructions, particularly under challenging textures and ambiguous viewpoints.
Diffusion models are powerful generative frameworks for producing high-quality images by denoising latent variables from random noise. However, training with likelihood-based objectives, such as denoising score matching, can lead to locally over-smoothed high-frequency details, including fine textures and sharp edges, thereby limiting perceptual fidelity and structural detail. Adversarial training with GANs enhances sharpness but typically requires additional discriminator networks, increasing computational costs and destabilizing training. To this end, we propose Latent Diffusion Generative Adversarial Networks (LD-GAN), a novel framework that seamlessly integrates adversarial learning into diffusion models without modifying their original pipeline. LD-GAN leverages the pretrained variational autoencoder (VAE) in latent diffusion models as an energy-based discriminator, enabling adversarial training without extra parameters and preserving the structured latent priors learned from large datasets. We also introduce a structural consistency energy that aligns encoder and decoder feature representations, thereby enhancing perceptual quality and compatibility with the pretrained latent space. Extensive experiments demonstrate that LD-GAN significantly improves sample fidelity, perceptual sharpness, and diversity over state-of-the-art baseline methods across various generation tasks while ensuring efficient training dynamics.