Playable Video Generation (2101.12195v1)

Abstract: This paper introduces the unsupervised learning problem of playable video generation (PVG). In PVG, we aim at allowing a user to control the generated video by selecting a discrete action at every time step as when playing a video game. The difficulty of the task lies both in learning semantically consistent actions and in generating realistic videos conditioned on the user input. We propose a novel framework for PVG that is trained in a self-supervised manner on a large dataset of unlabelled videos. We employ an encoder-decoder architecture where the predicted action labels act as bottleneck. The network is constrained to learn a rich action space using, as main driving loss, a reconstruction loss on the generated video. We demonstrate the effectiveness of the proposed approach on several datasets with wide environment variety. Further details, code and examples are available on our project page willi-menapace.github.io/playable-video-generation-website.

• The paper introduces an unsupervised framework (CADDY) that learns discrete action spaces from unannotated videos for interactive video synthesis.
• It employs an encoder-decoder architecture with a dynamics model and information-theoretic objectives to predict coherent and semantically consistent action transitions.
• Empirical results on BAIR, Atari, and Tennis datasets show that the method outperforms state-of-the-art baselines, indicating significant practical implications.

Playable Video Generation: An Unsupervised Learning Approach

The paper, "Playable Video Generation," presents a novel framework for enabling interactive video synthesis by proposing an unsupervised learning model that can effectively generate videos conditioned on user-provided action inputs. This approach introduces the concept of Playable Video Generation (PVG), wherein users can steer video content generation incrementally, drawing a parallel to the interactive nature of video games. The essence of this mechanism is rooted in the automatic learning of discrete action spaces from video datasets that lack annotated action labels, setting it apart from traditional action-conditioned video prediction systems which rely heavily on annotated data for effective training.

Framework Overview and Methodology

The authors propose a self-supervised framework called CADDY (Clustering for Action Decomposition and DiscoverY). The architecture is primarily composed of an encoder-decoder network augmented with a dynamics model, facilitating the understanding and prediction of state transitions given discrete action inputs. Central to this architecture is the action network, which consists of an action state network used to infer the posterior distribution of action embeddings and is integral in predicting discrete action labels. A unique aspect of this architecture is the incorporation of the action variability component, which captures fine-grained variations in action beyond the coarse-grained discrete labels, thereby enabling the modeling of inherent stochasticity present in video data.

Training is executed using a combination of reconstruction losses that guide the network to maintain fidelity in generated frames, alongside information-theoretic objectives that maximize mutual information between predicted and reconstructed action distributions, fostering robustness and minimizing collapse to trivial solutions.

Empirical Evaluation and Results

The authors conduct extensive empirical studies on three distinct datasets—BAIR robot pushing, Atari Breakout, and Tennis—quantitatively showcasing the effectiveness of the proposed framework. The model demonstrates superior performance over state-of-the-art video generation baselines like SAVP and MoCoGAN by exhibiting more coherent and semantically consistent actions that align well with the transitions induced by discrete labels, evidenced through metrics such as LPIPS, FVD, and FID.

On action space quality metrics like $\Delta$-MSE and $\Delta$-Acc, CADDY significantly outperforms other contenders, reflecting the efficacy of the clustering approach to capture and delineate meaningful action classes unfettered by explicit supervision. Additionally, user studies complement the quantitative results, indicating higher agreement and precision in associating user-generated actions with observed transitions.

Theoretical and Practical Implications

The implications of this research reach beyond mere methodological novelty. Theoretically, this unsupervised framework challenges the preconception that large-scale annotated datasets are quintessential for effective action-conditioned video synthesis. Practically, it offers a scalable alternative adaptable to various real-world scenarios where annotated data may be scarce or entirely unavailable, potentially revolutionizing interactive media, simulation environments, and automated content creation.

Future Directions

While the presented framework marks a significant advancement in unsupervised video synthesis, several avenues remain ripe for exploration. Extending PVG to multi-agent environments stands as a natural progression, which harbors the potential for more complex and engaging user interactions. Additionally, integrating mechanisms to handle higher-dimensional action spaces and more intricate scene dynamics could further enhance the applicability and versatility of the framework.

In conclusion, this paper lays a foundational and innovative step in playable video generation, furnishing the AI research community with fresh insights and tools for advancing autonomous, interactive video content creation.