In recent years, diffusion models have advanced text-to-video generation, prompting exploration into extending these capabilities to video content. The Scene and Motion Conditional Diffusion (SMCD) model integrates semantic and motion cues within a diffusion framework. By combining both types of inputs, SMCD significantly improves video quality, motion precision, and semantic coherence. The model aims to generate videos where objects exhibit precise motion aligned with the provided image context. Experimental results demonstrate the effectiveness of integrating diverse control signals in enhancing video generation outcomes.
Diffusion models transform data into Gaussian noise through forward and reverse processes. Video generation with diffusion models has seen advancements like the 3D diffusion UNet and temporal attention mechanisms. Customized generation focuses on aligning with user preferences, expanding from images to videos conditioned on various sequences like edges, depth, and segmentation.
SMCD introduces specialized modules for motion integration (MIM) and dual image integration (DIIM). These modules enhance the diffusion UNet by processing object trajectories and image conditions separately. A two-stage training pipeline refines the model's proficiency in managing object locations within single images before considering temporal information.
Evaluation metrics include FVD for video quality assessment, CLIP-SIM for text-image similarity scores, FFFDINO for first frame fidelity evaluation, and grounding accuracy metrics like SR50/SR75/AO. Comparative analysis of different image integration strategies highlights the effectiveness of combining zero-convolutional layers with gated cross-attention mechanisms in SMCD.
Ablative studies show that integrating both MIM and DIIM modules leads to optimal outcomes in generating high-quality videos aligned with specified conditions. Qualitative results further demonstrate SMCD's ability to maintain semantic consistency while accurately following motion dynamics defined by bounding box sequences.
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by Mingxiao Li,... at arxiv.org 03-18-2024
https://arxiv.org/pdf/2403.10179.pdfDeeper Inquiries