From Paths to Polished Performance: Generating Robotic Videos with Diffusion

Author: Denis Avetisyan

Researchers have developed a new method to create realistic robotic manipulation videos directly from trajectory data, enhancing both visual fidelity and task learning.

Object manipulation leverages tracked trajectories and depth estimation, aligning DINOv2 features with augmented text prompts to inform a modal policy model that predicts precise robot joint angles and gripper states.

Draw2Act leverages depth-encoded trajectories and DINOv2 features within a video diffusion model for improved multimodal robotic video generation.

While video diffusion models offer promising avenues for simulating robotic manipulation, achieving precise control over generated actions remains a significant challenge. To address this, we introduce DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos, a novel framework that leverages depth-aware trajectory representations and cross-modality attention within a diffusion model. By extracting and injecting multi-faceted information-including depth, semantics, shape, and motion-from input trajectories, DRAW2ACT generates more realistic and consistent robotic demonstration videos, ultimately improving downstream task performance. Could this approach unlock a new generation of robot learning systems capable of generalizing from limited demonstrations?

The Challenge of Embodied Intelligence: Reconciling Simulation and Reality

The creation of convincing robotic manipulation videos presents a significant hurdle in the advancement of both robotics and artificial intelligence. Unlike rendering simulations, generating video of physical robotic systems demands a reconciliation of visual realism with the precise execution of complex movements. Current research focuses on bridging the gap between simulated environments and the unpredictable nuances of the real world, where lighting, material properties, and unforeseen collisions can all impact performance. Success in this area isn’t merely about aesthetic fidelity; it requires the development of systems that can reliably predict and display how a robot will interact with objects, grasp them securely, and navigate dynamic environments – ultimately enabling more effective robot learning, control, and human-robot collaboration.

Current approaches to robotic video generation frequently encounter difficulties in maintaining geometric fidelity and accurate movement paths, hindering their deployment in real-world scenarios. Many systems struggle to consistently represent the spatial relationships between objects throughout a sequence, resulting in visually jarring distortions or implausible interactions. Similarly, precise trajectory following – ensuring the robot arm or end-effector moves along a planned, collision-free path – proves challenging due to limitations in modeling dynamic systems and accounting for physical constraints. This lack of consistency not only diminishes the realism of generated videos but also raises significant concerns about the safety and reliability of deploying such systems for robotic control or task planning; a robot trained on geometrically inconsistent data may exhibit unpredictable or erroneous behavior when interacting with the physical world.

Generating convincing robotic manipulation videos demands more than simply stitching together plausible images; it requires a deep understanding of three-dimensional space and the intricacies of how objects interact within it. Current approaches often falter because they treat video generation as a purely visual problem, overlooking the underlying physics and geometry. Models capable of accurately predicting object trajectories, contact forces, and deformation are crucial for creating realistic simulations. These models must not only render visually appealing scenes but also ensure that the robot’s actions are physically plausible and consistent with the environment. Successfully capturing these complex interactions-like a robotic hand grasping and manipulating a deformable object-hinges on the development of algorithms that reason about both the visual appearance and the underlying physical properties of the world, paving the way for more robust and reliable robotic systems.

This comparison highlights the varying qualities of trajectories generated by different video generation approaches.

Draw2Act: Precise Control Through Multimodal Diffusion

Draw2Act addresses limitations in existing video diffusion models regarding precise trajectory following by introducing a novel framework for enhanced control. Traditional diffusion models often struggle to accurately reproduce desired movements within generated video sequences. Draw2Act improves upon this by directly incorporating trajectory information into the diffusion process, enabling more accurate and consistent reproduction of complex robotic actions and movements. This is achieved through a conditioning mechanism that guides the video generation process, ensuring the output adheres to the specified trajectory while maintaining visual fidelity and realism.

Draw2Act utilizes a multi-faceted approach to trajectory representation for improved video generation control. Specifically, the framework encodes 3D trajectories using depth information, providing geometric context for robotic movements. Simultaneously, it incorporates high-level semantic features extracted via the DINOv2 model, capturing object recognition and scene understanding. These two trajectory representations – depth-encoded 3D data and DINOv2 features – are then used to condition the video diffusion process, enabling the model to generate video frames that accurately follow the desired trajectory and maintain semantic consistency with the scene.

Draw2Act leverages both RGB and depth video streams as conditioning inputs to the diffusion model, facilitating accurate robotic action control and maintaining geometric fidelity. Utilizing depth information, in addition to standard RGB imagery, provides the model with explicit 3D spatial reasoning capabilities. This allows Draw2Act to generate video frames that are consistent with the observed trajectory and the physical structure of the environment. The incorporation of depth data minimizes distortions and ensures that generated robotic movements adhere to realistic geometric constraints, resulting in more physically plausible and controllable video outputs.

Coordinate Augmented Text Captions enhance diffusion model control by incorporating pixel-level spatial information directly into the text conditioning. Traditional text prompts lack precise localization; these augmented captions address this by associating textual descriptions with specific $x, y$ coordinates within the reference image or video frame. This allows the diffusion process to understand where an action should occur, not just what action to perform. The system learns to correlate textual phrases with corresponding pixel locations, enabling finer-grained control over generated content and improving the accuracy of trajectory following, particularly in scenarios requiring precise robotic manipulation or interaction with specific objects in a scene.

The model architecture extracts trajectory representations-including DINOv2 features, pixel coordinates, and depth-aware imagery-and fuses them with DINOv2 layers before processing concatenated RGB and depth frames to enable multimodal output generation.

Under the Hood: Architectural Foundations for Realistic Generation

The system’s video generation relies on a Video Diffusion Model, a probabilistic generative model trained to reverse a gradual noising process. This model is specifically built upon the DiT (Diffusion Transformer) architecture, which replaces the convolutional layers typically found in diffusion models with transformer layers. This architectural choice enables more efficient denoising, particularly for longer video sequences, by leveraging the attention mechanism to model long-range dependencies within the video data. The DiT implementation facilitates parallel processing and improved scalability compared to traditional convolutional approaches, resulting in faster training and inference times for high-resolution video generation.

Video Depth Anything is utilized to generate per-frame depth maps from standard RGB video input. This process estimates the distance of each pixel in the image from the camera, creating a pseudo-3D representation of the scene. The resulting depth maps are crucial for providing spatial context, enabling the system to understand the relative positions of objects and surfaces within the video. This 3D context is essential for realistic video generation, as it allows the model to accurately synthesize novel views and maintain consistent object geometry across frames, even in the absence of explicit 3D models.

Object detection and tracking within the system are implemented using a pipeline integrating Grounded-SAM and TrackAnything. Grounded-SAM performs zero-shot object detection based on textual prompts, identifying and segmenting objects of interest within each video frame. TrackAnything then builds upon these detections, associating the same object across consecutive frames to establish consistent object identities and trajectories. This combination allows for robust tracking even with occlusions or changes in appearance, maintaining a coherent representation of objects throughout the video sequence and providing the necessary data for subsequent generative processes.

A Variational Autoencoder (VAE) serves as a dimensionality reduction tool within the video generation pipeline. The VAE encodes input video frames into a lower-dimensional latent space, represented by a probability distribution, allowing for efficient storage and manipulation of video data. This encoding process learns a compressed representation, capturing the essential features of the video while discarding redundant information. Subsequent decoding reconstructs the video from this latent representation. Utilizing a VAE significantly reduces computational demands during both training and inference, as operations are performed on the compressed latent vectors rather than the full-resolution video frames. The probabilistic nature of the VAE also enables the generation of novel video frames by sampling from the learned latent distribution.

Quantifying Realism: Performance and Validation Metrics

Draw2Act achieves state-of-the-art performance in robotic manipulation video generation by surpassing existing methods in both realism and control. Quantitative evaluation, utilizing metrics such as Fréchet Video Distance (FVD), Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), and Peak Signal-to-Noise Ratio (PSNR), demonstrates improved geometric consistency and trajectory following. Specifically, Draw2Act attains the highest video quality, as measured by Motion Smoothness, and minimizes object trajectory error compared to baseline models. This superior performance is attributed to the model’s capacity to generate videos exhibiting both visual fidelity and accurate robotic action execution.

Generated video quality and diversity were assessed quantitatively using Frechet Video Distance (FVD), Structural Similarity Index Measure (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), and Peak Signal-to-Noise Ratio (PSNR). FVD evaluates the statistical similarity between generated and real video frames, with lower scores indicating higher fidelity. SSIM measures perceptual similarity by comparing luminance, contrast, and structure, ranging from -1 to 1, with 1 representing perfect similarity. LPIPS, another perceptual metric, calculates the distance between feature representations extracted from deep neural networks, with lower values indicating higher similarity. PSNR quantifies reconstruction error based on pixel intensity, expressed in decibels (dB); higher PSNR values denote better quality. These metrics provide objective measures of both the visual realism and the diversity of the generated robotic manipulation videos.

Quantitative evaluation demonstrates that Draw2Act generates robotic manipulation videos with superior geometric consistency and trajectory following when compared to existing baseline methods. Specifically, the generated videos achieve the highest scores on the Motion Smoothness (Mot. Smth.) metric, indicating more natural and fluid robot movements. Furthermore, Draw2Act exhibits a significantly lower Object Trajectory Error, quantifying improved accuracy in replicating the desired object paths throughout the simulated manipulation tasks. These results, obtained through rigorous testing, confirm the model’s ability to generate videos that not only appear realistic but also accurately reflect the intended robotic actions.

The integration of multimodal inputs – specifically depth and semantic features – into the Draw2Act framework demonstrably improves both the realism and controllability of generated robotic manipulation videos. Quantitative evaluation using metrics including Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity Index Measure (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Fréchet Video Distance (FVD) consistently indicates superior depth video quality when utilizing these inputs. Furthermore, the incorporation of depth and semantic data correlates directly with an increased task success rate, suggesting improved ability to generate videos representing feasible and accurate robotic actions. These results confirm that leveraging richer input modalities is critical for achieving high fidelity and functional correctness in generated robotic video sequences.

Ablation studies demonstrate the proposed control approach outperforms alternatives on the simulation dataset.

Beyond Simulation: Charting a Course for Future Research

The Draw2Act framework is poised for expansion beyond its current capabilities, with future research concentrating on tackling increasingly intricate robotic tasks and diverse environments. Current efforts are directed toward enabling the system to generate video sequences for scenarios demanding more sophisticated manipulation, navigation, and interaction with dynamic objects. This involves refining the underlying algorithms to handle greater degrees of freedom in robotic motion, as well as developing methods for representing and simulating more complex environmental features, such as varying lighting conditions, textured surfaces, and cluttered scenes. Ultimately, this progression aims to bridge the gap between simplified robotic demonstrations and the nuanced demands of real-world applications, fostering a more versatile and adaptable robotic video generation platform.

Efforts to refine robotic video generation are increasingly focused on imbuing systems with a more nuanced understanding of visual content, and the integration of DINOv2 represents a significant step in this direction. This advanced vision transformer excels at extracting rich semantic information from images, allowing robots to not only recognize objects but also to grasp their relationships and affordances within a scene. Consequently, generated videos demonstrate improved geometric consistency-objects maintain plausible shapes and positions-and more realistic interactions. By leveraging DINOv2’s ability to discern subtle visual cues, robotic systems can anticipate how objects should behave when manipulated, leading to simulations and training scenarios that are both more accurate and more compelling. This heightened semantic awareness promises to bridge the gap between virtual and real-world robotic behavior, fostering the development of more adaptable and intelligent machines.

Current robotic video generation often produces pre-defined sequences, but research indicates substantial benefits from incorporating real-time interactive control. Allowing users to directly refine generated trajectories opens possibilities for nuanced adjustments and corrections, addressing limitations in fully automated systems. This interactive approach could leverage user input – such as subtle guidance or constraint adjustments – to steer the robot’s actions, ensuring desired outcomes even in complex or unpredictable environments. The development of such mechanisms necessitates robust algorithms capable of seamlessly integrating human input with the underlying robotic simulation, potentially utilizing techniques like reinforcement learning from human feedback to optimize trajectory refinement and enhance the overall usability of robotic video generation tools.

The advent of robotic video generation, as demonstrated by systems like Draw2Act, promises a fundamental shift in how robots are developed and deployed. Current robotic simulation often struggles to accurately reflect the complexities of the physical world, leading to discrepancies between virtual training and real-world performance. This technology offers a pathway to create highly realistic virtual environments, effectively bridging that gap and enabling robots to learn and refine skills in a safe, cost-effective manner. Beyond training, these generated videos can serve as a powerful tool for robotic design, allowing engineers to visualize and test different scenarios before physical prototyping. Furthermore, the creation of realistic virtual environments populated by robots has significant implications for fields like entertainment, education, and remote operation, offering immersive experiences and enhanced control capabilities.

The Draw2Act framework, with its focus on depth-aware trajectories, embodies a principle of elegant system design. It’s not simply about generating robotic videos; it’s about composing a coherent visual narrative from controlled movement. This pursuit of harmony between trajectory data and visual output resonates with Fei-Fei Li’s observation: “AI is not about replacing humans; it’s about augmenting and amplifying human capabilities.” Draw2Act demonstrates this amplification by transforming simple trajectories into richly detailed demonstrations, suggesting that beauty-in this case, visual clarity and control-scales, while complexity does not. The framework’s ability to generate realistic robotic manipulation videos from minimal input underscores the power of refined data representation and a commitment to functional elegance.

Beyond the Simulated Hand

The elegance of Draw2Act lies in its attempt to bridge the gap between trajectory and visual fidelity, yet the interface still whispers of limitations. Current video diffusion models, even when conditioned on depth and DINOv2 features, often struggle with the subtle choreography of complex manipulation-the almost imperceptible adjustments that distinguish proficiency from clumsy imitation. The true test won’t be generating visually plausible motions, but crafting videos that believably reflect a robot solving a problem, adapting to unforeseen circumstances within the scene.

Future iterations must address the brittleness inherent in relying solely on pre-defined trajectories. The system currently feels like a skilled performer reciting a memorized piece; what happens when the sheet music is altered mid-performance? Investigating methods to incorporate real-time feedback, perhaps through reinforcement learning woven into the diffusion process, could allow the generated videos to evolve beyond mere demonstrations and into expressions of adaptive robotic intelligence.

Ultimately, the field seeks not simply to show robotic manipulation, but to teach it. The generated videos represent a potentially rich dataset for imitation learning, but only if they embody the nuanced, often unspoken, principles of efficient and robust action. The harmony between trajectory control and visual realism is a beautiful start, but the true symphony awaits-one where the robot’s actions sing of genuine understanding.

Original article: https://arxiv.org/pdf/2512.14217.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/