Fashion photography is widely used on online platforms such as social media and e-commerce websites. However, as static images, they can limit their ability to provide comprehensive information about clothing, especially how it fits and moves on a person’s body.
In contrast, fashion videos offer a more complete and immersive experience, showcasing fabric textures, how they drape and flow, and other important details that are difficult to capture in still photography.
Fashion videos are an invaluable resource for consumers making informed purchasing decisions. They give shoppers a closer look at the actual clothes to help them better assess whether they suit their needs and tastes. Still, many brands and retailers primarily use photography to showcase their products. As the demand for more engaging and informative content continues to grow, the production of high-quality fashion videos is likely to increase across the industry.
A novel way to address these problems comes from artificial intelligence (AI). It’s called DreamPose and represents a novel approach to transforming fashion photography into lifelike animated videos.
This method involves a diffuse video synthesis model built on Stable Diffusion. By providing one or more images of a human girlfriend and a corresponding pose sequence, DreamPose can generate realistic, high-fidelity videos of subjects in motion. Here’s an overview of that workflow:
The task of generating high-quality, realistic videos from images presents several challenges. While the image diffusion model shows impressive results in terms of quality and fidelity, the same cannot be said for the video diffusion model. Such models are often limited to generating simple motion or cartoon-like visuals. In addition, existing video diffusion models have several problems, such as lack of temporal consistency, motion jitter, lack of realism, and limited motion control of the target video. Some of these limitations are due to the fact that existing models are tuned primarily based on text rather than other signals such as movement. This gives you finer control.
In contrast, DreamPose leverages image and pose adjustment schemes to improve appearance fidelity and frame-to-frame consistency. This approach overcomes many of the shortcomings of existing video diffusion models. Furthermore, it enables the production of high-quality video that accurately captures the movement and appearance of the input target.
This model is fine-tuned from a pre-trained image diffusion model that is very effective at modeling the distribution of natural images. Such a model can simplify the task of animating images by identifying subspaces of natural images that match the conditioning signal. To achieve this, the Stable Diffusion architecture has changed. Specifically, we redesign the encoder and adjustment mechanism to support adjustment of aligned and unaligned poses.
Furthermore, it includes a two-step fine-tuning process involving fine-tuning the UNet and VAE components using one or more input images. This approach optimizes the model to produce realistic, high-quality videos that accurately capture the appearance and movement of the input subject.
Some examples of generated results reported by the authors of this work are shown in the figure below. Additionally, the diagram includes a comparison of DreamPose and state-of-the-art.
This was an overview of DreamPose, a new AI framework that synthesizes photorealistic fashion videos from a single input image. If you are interested, you can learn more about this technique at the link below.
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Daniele Lorenzi has an M.Sc. He completed his ICT Bachelor’s Degree in Internet and Multimedia Engineering at the University of Padua, Italy in 2021. He has his Ph.D. Alpen-Adria-Universität (AAU) Candidate for the Information Technology Institute (ITEC) in Klagenfurt. He currently works at the Christian Doppler Laboratory ATHENA and his research interests include adaptive video streaming, immersive media, machine learning and his QoS/QoE assessment.
