i have a project i want you to code - 

'''
\section{Introduction}

Tools for editing talking-head video using transcripts
have made it possible to easily remove filler words, emphasize phrases,
correct mistakes, and try different wordings of the
speech\,\cite{berthouzoz12,Fried_2019,thies2019neural,10.1145/3072959.3073640}.
%
%% Other tools use a new audio recording to puppeteer a talking-head
%% \cite{bregler97,10.1145/3072959.3073640,thies2019neural}. Such tools
%% can be extended via a text-to-speech system to allow transcript based
%% editing \cite{thies2019neural} or to summarize recordings
%% \cite{10.1145/3072959.3073640}.
%%
%\Ohad{I tried to make the statement more accurate by splitting into two groups. This way we can also talk about a few more audio based methods if we want to.}
% \ohad{Bregler and Obama are audio based. Neural Voice Puppetry is mostly audio based but also mentions text. I would remove Bregler and Obama. ``Time aligned'' does not apply to NVP, and also it does not require lots of data which clashes with the rest of the intro; I would remove NVP (and discuss it later on).} 
Many of these tools can synthesize high-quality results that
closely match the appearance of the unedited video.
%
Such tools have the potential to enable a variety of
post-capture editing applications including re-phrasing dialogue in a
film scene, dubbing commercials to a new language, developing
dialogue for a conversational video assistant, and fixing wording
errors in an online lecture.

Yet, current transcript-based video editing tools are impractical
for use in many real-world editing scenarios for four main reasons.
% \maneesh{May want to rearrange put slow feedback first -- not sure.}
%\david{Put slow feedback first to be consistent with video presentation.}

\vspace{0.5em}
\noindent
{\bf \em (1) Slow feedback loop hinders iterative editing.}
Synthesizing the edited result at high-quality is often extremely
slow.  For example, while viewers report that Fried et
al.'s\,\shortcite{Fried_2019} results appear very realistic, their approach
takes hours to generate a few seconds of edited video.  The slow
feedback loop --- time between specifying an edit and seeing the
result --- significantly hinders iterative editing (e.g. trying
different phrasings of dialogue).

\vspace{0.5em}
\noindent
{\bf \em (2) Require hours of target talking-head video.}  To
produce realistic results, many of these
tools\,\cite{Fried_2019,10.1145/3072959.3073640} require
hours of video of the target talking-head
actor. Some tools further require the actor to speak a set of
specialized phrases (e.g. TIMIT corpus\,\cite{garofolo1993timit}).
%\maneesh{Also is there a
%  requirement that the actor be captured in the same environment with
%  a static camera?  Maybe should reference the ones that have this
%  requirement. We should at least know which of them have this
%  requirement.}
%\ohad{We need to compose a list of all the papers
%    we want to mention in the intro. If we want more than a couple, it
%    will have to include audio based and perhaps video2video stuff,
% which means we need to tweak the first paragraph a bit.}
  In practice however, many video editing projects lack access to such
  large amounts of target actor video.


\vspace{0.5em}  
\noindent
{\bf \em (3) Missing controls for refining results.}
  None of these editing tools provide controls for manually refining the lip
  motions of the synthesized results, making it impossible to fix
  objectionable artifacts these editing tools sometimes generate (e.g. mouth
  doesn't fully close on \textbackslash m, \textbackslash b,
  \textbackslash p phonemes). 

\vspace{0.5em}  
\noindent
    {\bf \em (4) Missing controls for adjusting non-verbal
      performance.}  None of these editing tools include controls for
    adjusting the target actor's non-verbal performance by inserting
    mouth gestures (e.g. a smile) or changing the overall speaking
    style (e.g. mumbling, energetic).
    %The lack of such performance
    %controls limits how much users can manipulate the video.
  
\vspace{0.5em}
In this work we present an iterative talking-head video
editing tool that explicitly addresses all four of these issues.
While our approach builds on the high-quality synthesis technique of
Fried~et~al.\,\shortcite{Fried_2019}, we make several new
contributions.  We significantly reduce the time required to
synthesize video (from hours to about 40 seconds for a 6 word edit) by
developing a fast algorithm for searching the source repository for
the desired lip motions.  We lower the data requirement on the target
actor video (2-3 minutes are usually enough) by leveraging a large
repository of video from a source actor and use a new self-supervised
neural retargeting technique to transfer their lip motions to the
target actor.  We provide controls to refine results by allowing users
to smooth over jumpy transitions and force mouth closure on the
results of the automated synthesis pipeline. Finally, we enable
insertion of non-verbal mouth gestures with the same text interface,
as well as controls to switch between different speaking styles by
using a version of the source repository with the desired style.
%\maneesh{Perhaps we should be consistent and just call our
%retargeting ``neural retargeting'' everywhere. Perhaps call it
%self-supervised neural retargeting.}

As shown in Figure~\ref{fig:teaser} our tool enables an iterative
editing workflow. Given a short video of the target actor, the user
can edit the transcript and our tool synthesizes the corresponding
video in under a minute. 
The user can inspect the feedback and further adjust wording, refine the
lip motions and/or insert mouth gestures and quickly see how the
adjustment affects the synthesized video.
%
%\maneesh{Maybe move note to very end of intro?}
Note that our work focuses on generating video from text;
to obtain the corresponding speech audio, we rely on either having
access to the actor speaking the new content (e.g. from a prerecorded library of the actor's speech or recorded by the actor in real-time during editing), 
text-to-speech voice synthesis\,\cite{wavenet2016} or voice
cloning\,\cite{kumar2019lyrebird, jia2018sv2tts}. 
%Once a transcript has been finalized, the user may choose to
%re-capture an audio recording of the target actor saying the finalized
%lines and use our tool to generate the video to match the actor's
%final speech.
%\david{Maybe talk about the real-voice iteration mode here.} \maneesh{We probably should say this and combine somehow with previous sentence. Probably should also cite lyrebird for tts or voice cloning.}
%\ohad{I would kill final sentence and instead of a whole paragraph about results and eval, try to make that our concluding sentence. Something like "We demonstrate various editing scenarios using our tool, and evaluate result quality in two user studies"}
%Our videos include many results with a synthetic
%voice as well as the target actor's real voice for the finalized lines.
%\maneesh{Kill prev sentence?}
%

We demonstrate a variety of iterative editing sessions facilitated by
our tool and we conduct user studies which show that our synthesized
results are rated as ``real'' for 56.2\% of the sentence-long edits
and for 64.9\% of the phrase-long edits -- slightly better than the
previous state-of-the-art approach of Fried~et~al.\,\shortcite{Fried_2019}.
%\maneesh{User studies really
%  only compare to Fried19 and are inconclusive w.r.t NVP, so I changes prev. sentence. Though it is awkward to talk about Fried19 as previous state-of-the-art. -- could call it ``high-quality approach''}
Together
these results suggest that our algorithm provides the speed, data
efficiency and controls necessary for a practical iterative editing
workflow while maintaining high-quality synthesis results.
% we conduct two user studies which show that our tool
% compares favorably in quality with respect to baseline methods as well
% as previous techniques.
%% \maneesh{I would add a sentence or two about our user study and say that we are as good or better in quality than previous methods. (Perhaps something like what I added to the abstract -- though that should be made shorter and we can have it be a little longer here. The summative sentence from the abstract could move from there to here -- though data efficiency is not a great term. Could also say how we are really doing longer -- full sentence synthesis -- than previous methods.}
  %% \david{The distinction between short and long edits is mostly with respect to Fried19 and I think fits better in the evaluation section, which is already there (in user studies in \Cref{sec:user_study})}
  %% \maneesh{Not sure I follow your point David. Do you have a suggestion for how to state the user study results more succinctly?}


%\maneesh{Probably cut contributions statement.}
%% ------------ Old Intro   -----------------


%% Tools for editing talking-head video have made it possible to remove
%% filler words, correct mistakes in speech, re-dub video to a new
%% language, and try different phrasings of film
%% dialogue\;\cite{floraine,videorewrite,obama,fried19,etc.}. \maneesh{should
%%   we keep redubbing?} 
%% While these tools can synthesize high-quality results that are often
%% indistinguishable from unedited video, 
%% they are impractical
%% for use in many real-world editing scenarios for three main reasons;
%% \maneesh{Probably should keep only positives in first paragraph. Add applications to end of paragraph, And start next paragraph with negatives.}

%% (1) Many of these tools require significant amounts (tens to hundreds
%% of minutes) of training video of the target actor, and in some cases
%% require the actor to speak a set of specialized phrases (e.g. the
%% TIMIT phrases). \maneesh{Is this true of all tools. Maybe should
%%   reference the ones that have this requirement. We should at least
%%   know which of them have this requirement.} Many video editing
%% projects lack access to such large amounts of data.

%% (2) Synthesizing high-quality video (with sme of these tools) is often
%% extremely slow, taking multiple hours to render a few seconds of
%% edited video.  The slow feedback loop (time between specifying an edit
%% and seeing the result), significantly hinders iterative editing
%% (e.g. testing different phrasings of dialogue). \maneesh{Again should
%%   limit to some of the tools and cite them.}
%% %While iteratively trying different edits (e.g. different phrasings of
%% %dialogue) is essential

%% (3) None of these editing tools provide contols for refining the lip
%% motions of the synthesized results and few of them include controls
%% for inserting non-verbal face gestures (e.g. a smile) into the target
%% actor's performance. The lack of refinement controls makes it
%% impossible to fix errors that these synthesis tools sometimes generate
%% (e.g. mouth doesn't fully close on \textbackslash m, \textbackslash b,
%% \textbackslash p phonemes). The lack of gesture controls makes it
%% difficult to adjust non-verbal aspects of the performance.
%% \maneesh{Again should limit to some of the tools and cite them. May
%%   want to split refinement vs. facial gestures.}

%% In this work we present an interactive talking-head video editing tool
%% that explictly addresses all three of these issues. We .... \maneesh{explain in a sentence how we address each one.}






% -------------


% The main technical contributions of this paper are:

% \begin{itemize}
%     \item A learning-based retargeting method that can translate mouth expressions for speech from a source actor to a target actor, even when two actors speak different scripts and when there is as little as a few minutes of data for target actor.
%     \item A fast search strategy for finding snippets of phoneme sequences to assemble into synthesized video that is up to 1000 times faster than state-of-the-art and still maintains comparable quality of synthesis results.
%     \item A tool that combines both components above to enable text-based editing of talking head video. It is effective even on short videos of only a few minutes, and it is fast \ohad{I think the following should be its own bullet point} and allows user-directed iterative refinement of results and insertion of non-verbal mouth gestures such as smile.
% \end{itemize}

% \maneesh{The contributions listed above feel out of order. I think we also need to connect them with the overall goal of interactive editing. Here is how I might write them. But the bold headings need to be made to fit in a single line.}

%% To enable interactive editing of talking-head video our work makes
%% three main technical contributions:

%% \begin{itemize}
%%   \item {\bf \em Fast phoneme sequence search.} We present a search strategy for finding snippets of phoneme sequences to assemble into synthesized video that is up to 1000 times faster than the previous state-of-the-art methods while maintaining comparable synthesis quality. \maneesh{When we have gesture labels do we search over them as well?}

%%    \item {\bf \em Retargeting method requiring only a few minutes of target actor.} \ohad{Suggested title: Expression retargetting with limited data.} We
%%      develop a learning-based retargeting method that can translate
%%      mouth expressions from a source actor to a target actor, even
%%      when two actors speak different scripts \maneesh{I think previous
%%        work doesn't require same script - does it? - can we cut that
%%        point?} and when there is as little as a few minutes of data
%%      for target actor. \maneesh{We show that this approach allows our
%%        work to apply to footage from existing film scenes}.

%%    \item {\bf \em Interactive controls for refinement and insertion of
%%      mouth gestures.} \ohad{Suggested title: Refinement and gesture control.} We combine these two components into an
%%      interactive text-based editing tool that
%%       allows user-directed iterative refinement of results and
%%       insertion of non-verbal mouth gestures such as a smile.

%% \end{itemize}


% LocalWords:  TIMIT et al retargeting pre summative