Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input

Abstract: Event cameras are advantageous for tasks that require vision sensors with low-latency and sparse output responses. However, the development of deep network algorithms using event cameras has been slow because of the lack of large labelled event camera datasets for network training. This paper reports a method for creating new labelled event datasets by using a text-to-X model, where X is one or multiple output modalities, in the case of this work, events. Our proposed text-to-events model produces synthetic event frames directly from text prompts. It uses an autoencoder which is trained to produce sparse event frames representing event camera outputs. By combining the pretrained autoencoder with a diffusion model architecture, the new text-to-events model is able to generate smooth synthetic event streams of moving objects. The autoencoder was first trained on an event camera dataset of diverse scenes. In the combined training with the diffusion model, the DVS gesture dataset was used. We demonstrate that the model can generate realistic event sequences of human gestures prompted by different text statements. The classification accuracy of the generated sequences, using a classifier trained on the real dataset, ranges between 42% to 92%, depending on the gesture group. The results demonstrate the capability of this method in synthesizing event datasets.

• The paper introduces Text-to-Events, a novel model combining a pretrained VLM, diffusion model, and autoencoder to directly synthesize realistic event camera streams from text prompts.
• Experimental results on the DVS gesture dataset show synthetic data supports downstream classification tasks with 42%-92% accuracy, validating its fidelity to real event streams.
• This method overcomes dataset scarcity for event cameras, promising to accelerate development of high-speed vision systems in robotics, automotive, and immersive reality applications.

Text-to-Events: Synthetic Event Camera Streams from Conditional Text Input

The paper describes an innovative approach to generating synthetic datasets for event cameras through a model called "Text-to-Events," which is designed to produce event camera outputs based on conditional text prompts. Event cameras, which offer advantages such as low-latency and sparse data capture, have been limited in their application due to the lack of comprehensive labeled datasets necessary for the effective training of deep learning algorithms. This paper proposes a method that aims to mitigate these limitations by synthesizing realistic event sequences without the intermediate step of generating intensity frames, a common requirement in other modalities.

Methodology

The proposed method integrates a pretrained text-to-video model (VLM) with a diffusion model architecture, utilizing an autoencoder to produce sparse event frames. Initially, the autoencoder is trained on real event datasets to develop a model capable of generating event frames that accurately mimic the outputs from event cameras. This autoencoder is then combined with a diffusion model, which facilitates the generation of smooth synthetic event streams from conditional text inputs.

The model leverages:

1. Autoencoder Architecture: Pretrained to produce sparse event frames representing event camera outputs, ensuring effective capture of high-temporal dynamics.
2. Latent Diffusion Model: Ensures the generation of realistic motion sequences, trained on the DVS gesture dataset to learn diverse gesture movements.
3. Large-scale Contrastive Language-Video Model (VLM): Processes text inputs to provide the embedding used by the diffusion model, thereby generating corresponding event sequences.

The model's architecture supports progressive training, deploying a multi-stage approach to enhance autoencoder performance and reduce risks like posterior collapse, commonly encountered during the training of VAEs.

Experimental Results

The model was trained on the DVS128 gesture dataset, with results demonstrating a classification accuracy ranging between 42% and 92% depending on the gesture category. The classification task was executed using a separately trained classifier on real dataset samples, validating the capability of the generated data to mirror real event streams. This range of performance reflects the complexity inherent in generating certain gesture types, specifically those involving large-scale rotational movements.

Visual analysis of the generated outputs shows that the model accurately reproduces the temporal and spatial dynamics found in natural gesture sequences. This capability is foundational for expanding event camera applications without relying on scarce real-world datasets.

Implications and Future Work

The research outlined in the paper holds considerable implications for the development of vision-based AI systems, particularly those requiring efficiency in environments with dynamic visual information. By offering a mechanism to generate robust labeled datasets, it alleviates a significant bottleneck in the advancement of event camera technologies, promising to accelerate research and application deployment in fields such as robotics, automotive vision, and immersive reality.

Moving forward, expanding the model's capabilities to handle a wider array of text prompts and scenarios is critical. This expansion would entail training on more diverse datasets, possibly integrating concurrent advancements in LLMs to further enrich the semantical understanding and compositionality of the VLM embeddings. Additionally, aligning the generation with specific sensor characteristics could tailor the synthetic datasets to better mimic the noise and dynamic range observed in real-world recordings.

In summary, this research presents a novel and efficient approach to synthetic dataset creation for event cameras, establishing a foundation for future explorations in high-speed vision systems and representing a significant contribution to the sparsely explored domain of text-to-events generation.