CineMobile: On-Device Image-to-Video Diffusion for Cinematic Camera Motion Generation

2026-07-04

AI Summary

This work addresses the computational and memory bottlenecks hindering efficient, cinematic camera-motion video generation (e.g., bullet time, dolly zoom) from images on mobile devices. The authors propose a three-stage optimization strategy: distillation-guided pruning to obtain a compact model, combined diffusion distillation and reinforcement learning to compress the generator into four denoising steps, and mixed post-training quantization to reduce the model size below 1 GB. Built upon the Diffusion Transformer architecture, this approach achieves the first on-device image-to-video diffusion model capable of generating cinematic camera motions. Compared to the Wan 2.1 teacher model, it offers a 40× speedup in inference, enabling generation of 49-frame 480p videos on a MediaTek Dimensity 8400 chipset with only 20 seconds per denoising step and a peak memory footprint of 1.8 GB, significantly advancing practical high-quality video synthesis on mobile platforms.

Abstract

The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, slow motion, etc. While Diffusion Transformers (DiTs) exhibit strong performance in video generation, their large parameter sizes and multi-step iterative denoising processes lead to substantial computational overhead, making efficient generation on mobile devices challenging. We propose CineMobile to bridge the gap. In particular, CineMobile adopts a three-fold optimization strategy: (1) leveraging a distillation-guided pruning approach to derive a compact yet efficient model that retains the essential video generation capabilities required for cinematic effects; (2) optimizing the compressed model into a 4-step generator via a combination of diffusion distillation and reinforcement learning; (3) employing a hybrid post-training quantization strategy to compress the model footprint to under 1 GB. Experimental results show that compared to the teacher model with the Wan 2.1 architecture, CineMobile achieves a 40x speedup in generation while maintaining comparable visual quality. Specifically, CineMobile generates 49-frame 480p videos with a per-step denoising latency of 0.6s on an NVIDIA H200 GPU and 20s on the MediaTek Dimensity 8400 Ultimate 5G platform, with a peak memory usage of 1.8 GB, demonstrating its practical applicability for mobile-based image-to-video creation.

Problem

• image-to-video
• cinematic motion
• mobile device
• diffusion model
• computational efficiency

Innovation

• Diffusion Transformers
• model distillation
• pruning
• post-training quantization
• mobile video generation