Diffusion Transformer (DiT) is emerging as a cutting-edge trend in the landscape of generative diffusion models for image generation. Recently, masked-reconstruction strategies have been considered to improve the efficiency and semantic consistency in training DiT but suffer from deficiency in contextual information extraction. In this paper, we provide a new insight to reveal that noisy-to-noisy masked-reconstruction harms sufficient utilization of contextual information. We further demonstrate the insight with theoretical analysis and empirical study on the mutual information between unmasked and masked patches. Guided by such insight, we propose a novel training paradigm named MC-DiT for fully learning contextual information via diffusion denoising at different noise variances with clean-to-clean mask-reconstruction. Moreover, to avoid model collapse, we design two complementary branches of DiT decoders for enhancing the use of noisy patches and mitigating excessive reliance on clean patches in reconstruction. Extensive experimental results on 256$\times$256 and 512$\times$512 image generation on the ImageNet dataset demonstrate that the proposed MC-DiT achieves state-of-the-art performance in unconditional and conditional image generation with enhanced convergence speed.

Generalization ability of face anti-spoofing has been widely concerned in recent years. Existing domain generalization methods use adversarial learning or metric learning to extract invariant features across domains but are proved to be flawed from causal views. The learned domain-invariant features cannot generalize well to unseen real scenarios in both theoretical and practical senses. In this paper, we propose a novel method that learns Causal Representations for Face Anti-Spoofing (CRFAS). We first model the data generation process of face anti-spoofing via Structural Casual Model (SCM) and reveal that only the causal feature is capable of generalizing to unseen domains. On such basis, we extract causal features via back- door adjustment without prior assumptions rather than learn domain-invariant features as existing methods. Furthermore, we employ the supervised contrastive loss to generate more realistic counterfactual features for backdoor adjustment and improve the generalization ability of learned causal features. Extensive experiments on six cross-dataset testing scenarios demonstrate that CRFAS outperforms the state-of-the-art face anti-spoofing methods in terms of HTER and AUC in generalizing to unseen domains.