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You can drive vehicles on Google Maps. You can drive safely, ignore roads, park, race on a circuit, and travel around the world. You can play in any way you want!
This is a top-down view driving simulator. You can drive cars, buses, and trailers on small cources.
FrameSynthesis Inc. is a development company specializing in VR and interactive content. Our mission is to support the integration and widespread use of virtual reality in everyday life, primarily from a technical perspective.
Video Frame Synthesis using Deep Voxel Flow. We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation).
We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames.
During RFS, two reconstructed frames from DPB are input into STENet’s synthesis pipeline to synthesize an intermediate frame, treated as the virtual reference frame, and inserted into two RPLs. During PFE, two reconstructed frames are selected from DPB and input into STENet’s enhancement pipeline to alleviate their artifacts and distortions ...
In this paper, we propose an end-to-end deep network, Deep Voxel Flow (DVF), for video frame synthesis. Our method is able to copy pixels from existing video frames, rather than hallucinate them from scratch. On the other hand, our method can be trained in an unsupervised manner using any video.
We present a content-adaptive, low-complexity video frame synthesis algorithm. Our approach applies the dynamic convolutions content adaptation approach to the widely used frame synthesis algorithm IFRNet.
We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex.
DVF learns to synthesize a target frame from the input video. The target frame can either be in-between (interpolation) or subsequent to (extrapolation) the input video.