TRPO, PPO), and a critic that involves minimizing a closely connected objective.

In contrast, 3d pose estimation from a single image is ill-posed due to occlusion and depth ambiguities.

You guess is as good as mine as to why a film about blockchain technology needs to be filmed this way, but Doug Liman has been connected with plenty of unusual projects in the past, including a rumored Tom Cruise film that was supposed to film on the International Space Station.

AI is already making online swindles easier. It could get much worse. Some cybersecurity researchers say it's too early to worry about AI-orchestrated cyberattacks. Others say it could already be happening. Anton Cherepanov is always on the lookout for something interesting. And in late August last year, he spotted just that.

Dubbing is a post-production process of re-recording actors' dialogues, which is extensively used in filmmaking and video production. It is usually performed manually by professional voice actors who read lines with proper prosody, and in synchronization with the pre-recorded videos. In this work, we propose Neural Dubber, the first neural network model to solve a novel automatic video dubbing (AVD) task: synthesizing human speech synchronized with the given video from the text. Neural Dubber is a multi-modal text-to-speech (TTS) model that utilizes the lip movement in the video to control the prosody of the generated speech. Furthermore, an image-based speaker embedding (ISE) module is developed for the multi-speaker setting, which enables Neural Dubber to generate speech with a reasonable timbre according to the speaker's face. Experiments on the chemistry lecture single-speaker dataset and LRS2 multi-speaker dataset show that Neural Dubber can generate speech audios on par with state-of-the-art TTS models in terms of speech quality. Most importantly, both qualitative and quantitative evaluations show that Neural Dubber can control the prosody of synthesized speech by the video, and generate high-fidelity speech temporally synchronized with the video.

We present temporally abstract actor-critic (TAAC), a simple but effective off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework. TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor. Crucially, its act-or-repeat decision hinges on the actually sampled action instead of the expected behavior of the actor. This post-acting switching scheme let the overall policy make more informed decisions. TAAC has two important features: a) persistent exploration, and b) a new compare-through Q operator for multi-step TD backup, specially tailored to the action repetition scenario. We demonstrate TAAC's advantages over several strong baselines across 14 continuous control tasks. Our surprising finding reveals that while achieving top performance, TAAC is able to mine a significant number of repeated actions with the trained policy even on continuous tasks whose problem structures on the surface seem to repel action repetition. This suggests that aside from encouraging persistent exploration, action repetition can find its place in a good policy behavior. Code is available at https://github.com/hnyu/taac.

Requiem for a film-maker: Darren Aronofsky's AI revolutionary war series is a horror I f you happen to find yourself stumbling through Time magazine's YouTube account, perhaps because you are a time traveller from the 1970s who doesn't fully understand how the present works yet - then you will be presented with something that many believe represents the vanguard of entertainment as we know it. On This Day 1776 is a series of short videos depicting America's revolutionary war. What makes On This Day notable is that it was made by Darren Aronofsky's studio Primordial Soup. What also makes it interesting is that it was created with AI. The third thing that makes it interesting is that it is terrible.