Computer vision is one of the vital exciting and well-researched fields inside the AI community today, and despite the rapid enhancement of the pc vision models, a longstanding challenge that also troubles developers is image animation. Even today, image animation frameworks struggle to convert still images into their respective video counterparts that display natural dynamics while preserving the unique appearance of the pictures. Traditionally, image animation frameworks focus totally on animating natural scenes with domain-specific motions like human hair or body motions, or stochastic dynamics like fluids and clouds. Although this approach works to a certain extent, it does limit the applicability of those animation frameworks to more generic visual content.
Moreover, conventional image animation approaches concentrate totally on synthesizing oscillating and stochastic motions, or on customizing for specific object categories. Nevertheless, a notable flaw with the approach is the strong assumptions which are imposed on these methods that ultimately limits their applicability especially across general scenarios like open-domain image animation. Over the past few years, T2V or Text to Video models have demonstrated remarkable success in generating vivid and diverse videos using textual prompts, and this demonstration of T2V models is what forms the inspiration for the DynamiCrafter framework.
The DynamiCrafter framework is an try to overcome the present limitations of image animation models and expand their applicability to generic scenarios involving open-world images. The DynamiCrafter framework attempts to synthesize dynamic content for open-domain images, converting them into animated videos. The important thing idea behind DynamiCrafter is to include the image as guidance into the generative process in an try to utilize the motion prior of the already existing text to video diffusion models. For a given image, the DynamiCrafter model first implements a question transformer that projects the image right into a text-aligned wealthy context representation space, facilitating the video model to digest the image content in a compatible manner. Nevertheless, the DynamiCrafter model still struggles to preserve some visual details within the resultant videos, an issue that the DynamiCrafter model overcomes by feeding the complete image to the diffusion model by concatenating the image with the initial noises, due to this fact supplementing the model with more precise image information.
This text goals to cover the DynamiCrafter framework in depth, and we explore the mechanism, the methodology, the architecture of the framework together with its comparison with state-of-the-art image and video generation frameworks. So let’s start.
Animating a still image often offers a fascinating visual experience for the audience because it seems to bring the still image to life. Over time, quite a few frameworks have explored various methods of animating still images. Initial animation frameworks implemented physical simulation based approaches that focused on simulating the motion of specific objects. Nevertheless, as a consequence of the independent modeling of every object category, these approaches were neither effective nor they’d generalizability. To duplicate more realistic motions, reference-based methods emerged that transferred motion or appearance information from reference signals like videos to the synthesis process. Although reference based approaches delivered higher results with higher temporal coherence in comparison to simulation based approaches, they needed additional guidance that limited their practical applications.
In recent times, a majority of animation frameworks focus totally on animating natural scenes with stochastic, domain-specific or oscillating motions. Although the approach implemented by these frameworks work to a certain extent, the outcomes these frameworks generate are usually not satisfactory, with significant room for improvement. The remarkable results achieved by Text to Video generative models previously few years has inspired the developers of the DynamiCrafter framework to leverage the powerful generative capabilities of Text to Video models for image animation.
The important thing foundation of the DynamiCrafter framework is to include a conditional image in an attempt to control the video generation means of Text to Video diffusion models. Nevertheless, the last word goal of image animation still stays non-trivial since image animation requires preservation of details in addition to understanding visual contexts essential for creating dynamics. Nevertheless, multi-modal controllable video diffusion models like VideoComposer have attempted to enable video generation with visual guidance from a picture. Nevertheless, these approaches are usually not suitable for image animation since they either lead to abrupt temporal changes or low visual conformity to the input image owing to their less comprehensive image injection mechanisms. To counter this hurdle, the DyaniCrafter framework proposes a dual-stream injection approach, consisting of visual detail guidance, and text-aligned context representation. The twin-stream injection approach allows the DynamiCrafter framework to make sure the video diffusion model synthesizes detail-preserved dynamic content in a complementary manner.
For a given image, the DynamiCrafter framework first projects the image into the text-aligned context representation space using a specially designed context learning network. To be more specific, the context representation space consists of a learnable query transformer to further promote its adaptation to the diffusion models, and a pre-trained CLIP image encoder to extract text-aligned image features. The model then uses the wealthy context features using cross-attention layers, and the model uses gated fusion to mix these text features with the cross-attention layers. Nevertheless, this approach trades the learned context representations with text-aligned visual details that facilitates semantic understanding of image context allowing reasonable and vivid dynamics to be synthesized. Moreover, in an try to complement additional visual details, the framework concatenates the complete image with the initial noise to the diffusion model. Consequently, the dual-injection approach implemented by the DynamiCrafter framework guarantees visual conformity in addition to plausible dynamic content to the input image.
Moving along, diffusion models or DMs have demonstrated remarkable performance and generative prowess in T2I or Text to Image generation. To duplicate the success of T2I models to video generation, VDM or Video Diffusion Models are proposed that uses a space-time factorized U-Recent architecture in pixel space to model low-resolution videos. Transferring the learnings of T2I frameworks to T2V frameworks will assist in reducing the training costs. Although VDM or Video Diffusion Models have the flexibility to generate top quality videos, they only accept text prompts as the only semantic guidance that may not reflect a user’s true intentions or is perhaps vague. Nevertheless, the outcomes of a majority of VDM models rarely adhere to the input image and suffers from the unrealistic temporal variation issue. The DynamiCrafter approach is built upon text-conditioned Video Diffusion Models that leverage their wealthy dynamic prior for animating open-domain images. It does so by incorporating tailored designs for higher semantic understanding and conformity to the input image.
DynamiCrafter : Method and Architecture
For a given still image, the DyanmiCrafter framework attempts to animate the image to video i.e. produce a brief video clip. The video clip inherits the visual contents from the image, and exhibits natural dynamics. Nevertheless, there may be a possibility that the image might appear within the arbitrary location of the resulting frame sequence. The looks of a picture in an arbitrary location is a special type of challenge observed in image-conditioned video generation tasks with high visual conformity requirements. The DynamiCrafter framework overcomes this challenge by utilizing the generative priors of pre-trained video diffusion models.
Image Dynamics from Video Diffusion Prior
Often, open-domain text to video diffusion models are known to display dynamic visual content modeled conditioning on text descriptions. To animate a still image with Text to Video generative priors, the frameworks should first inject the visual information within the video generation process in a comprehensive manner. Moreover, for dynamic synthesis, the T2V model should digest the image for context understanding, while it must also give you the option to preserve the visual details within the generated videos.
Text Aligned Context Representation
To guide video generation with image context, the DynamiCrafter framework attempts to project the image into an aligned embedding space allowing the video model to make use of the image information in a compatible fashion. Following this, the DynamiCrafter framework employs the image encoder to extract image features from the input image for the reason that text embeddings are generated using a pre-trained CLIP text encoder. Now, although the worldwide semantic tokens from the CLIP image encoder are aligned with the image captions, it primarily represents the visual content on the semantic level, thus failing to capture the complete extent of the image. The DynamiCrafter framework implements full visual tokens from the last layer of the CLIP encoder to extract more complete information since these visual tokens reveal high-fidelity in conditional image generation tasks. Moreover, the framework employs context and text embeddings to interact with the U-Net intermediate features using the twin cross-attention layers. The design of this component facilitates the flexibility of the model to soak up image conditions in a layer-dependent manner. Moreover, for the reason that intermediate layers of the U-Net architecture associate more with object poses or shapes, it is predicted that the image features will influence the looks of the videos predominantly especially for the reason that two-end layers are more linked to appearance.
Visual Detail Guidance
The DyanmiCrafter framework employs rich-informative context representation that permits the video diffusion model in its architecture to provide videos that resemble the input image closely. Nevertheless, as demonstrated in the next image, the generated content might display some discrepancies owing to the limited capability of the pre-trained CLIP encoder to preserve the input information completely, because it has been designed to align language and visual features.
To reinforce visual conformity, the DynamiCrafter framework proposes to supply the video diffusion model with additional visual details extracted from the input image. To attain this, the DyanmiCrafter model concatenates the conditional image with per-frame initial noise and feeds them to the denoising U-Net component as guidance.
Training Paradigm
The DynamiCrafter framework integrates the conditional image through two complementary streams that play a major role intimately guidance and context control. To facilitate the identical, the DynamiCrafter model employs a three-step training process
- In step one, the model trains the image context representation network.
- Within the second step, the model adapts the image context representation network to the Text to Video model.
- Within the third and final step, the model fine-tunes the image context representation network jointly with the Visual Detail Guidance component.
To adapt image information for compatibility with the Text-to-Video (T2V) model, the DynamiCrafter framework suggests developing a context representation network, P, designed to capture text-aligned visual details from the given image. Recognizing that P requires many optimization steps for convergence, the framework’s approach involves initially training it using a less complicated Text-to-Image (T2I) model. This strategy allows the context representation network to focus on learning in regards to the image context before integrating it with the T2V model through joint training with P and the spatial layers, versus the temporal layers, of the T2V model.
To make sure T2V compatibility, the DyanmiCrafter framework merges the input image with per-frame noise, proceeding to fine-tune each P and the Visual Discrimination Model’s (VDM) spatial layers. This method is chosen to take care of the integrity of the T2V model’s existing temporal insights without the opposed effects of dense image merging, which could compromise performance and diverge from our primary goal. Furthermore, the framework employs a technique of randomly choosing a video frame because the image condition to realize two objectives: (i) to avoid the network developing a predictable pattern that directly associates the merged image with a selected frame location, and (ii) to encourage a more adaptable context representation by stopping the supply of overly rigid information for any particular frame.
DynamiCrafter : Experiments and Results
The DynamiCrafter framework first trains the context representation network and the image cross-attention layers on Stable Diffusion. The framework then replaces the Stable Diffusion component with VideoCrafter and further fine-tunes the context representation network and spatial layers for adaptation, and with image concatenation. At inference, the framework adopts the DDIM sampler with multi-condition classifier-free guidance. Moreover, to judge the temporal coherence and quality of the videos synthesized in each the temporal and spatial domains, the framework reports FVD or Frechet Video Distance, in addition to KVD or Kernel Video Distance, and evaluates the zero-shot performance on all of the methods of MSR-VTT and UCF-101 benchmarks. To analyze the perceptual conformity between the generated results and the input image, the framework introduces PIC or Perceptual Input Conformity, and adopts the perceptual distance metric DreamSim because the function of distance.
The next figure demonstrates the visual comparison of generated animated content with different styles and content.
As it could possibly be observed, amongst all different methods, the DynamiCrafter framework adheres to the input image condition well, and generates temporally coherent videos. The next table accommodates the statistics from a user study with 49 participants of the preference rate for Temporal Coherence (T.C), and Motion Quality (M.C) together with the choice rate for visual conformity to the input image. (I.C). As it could possibly be observed, the DynamiCrafter framework is capable of outperform existing methods by a substantial margin.
The next figure demonstrates the outcomes achieved using the dual-stream injection method and the training paradigm.
Final Thoughts
In this text, we’ve got talked about DynamiCrafter, an try to overcome the present limitations of image animation models and expand their applicability to generic scenarios involving open-world images. The DynamiCrafter framework attempts to synthesize dynamic content for open-domain images, converting them into animated videos. The important thing idea behind DynamiCrafter is to include the image as guidance into the generative process in an try to utilize the motion prior of the already existing text to video diffusion models. For a given image, the DynamiCrafter model first implements a question transformer that projects the image right into a text-aligned wealthy context representation space, facilitating the video model to digest the image content in a compatible manner. Nevertheless, the DynamiCrafter model still struggles to preserve some visual details within the resultant videos, an issue that the DynamiCrafter model overcomes by feeding the complete image to the diffusion model by concatenating the image with the initial noises, due to this fact supplementing the model with more precise image information.
